Books 2015(1 publication) [publication]Costa, E. , "Programação em Python: fundamentos e resolução de problemas", vol. -, 2015 2008(1 publication) [publication]Costa, E. and Simões, A. , "Inteligência Artificial: fundamentos e aplicações", 2008 [citation][year=2015]Lopes, J. A. T. (2015). Generalização cartográfica com recurso a inteligência artificial. [citation][year=2014]Grosso, M. M. Q. M. (2014). Introdução à inteligência artificial com recurso à programação visual (Doctoral dissertation). [citation][year=2014]Dorigan, J. A., & Miranda de Barros, R. (2014). A process model for standardization and increase in the requirements quality. Latin America Transactions, IEEE (Revista IEEE America Latina), 12(8), 1502-1507. [citation][year=2013]Lopes, T., Antonio, J., & Catalao, J. (2013, June). A new method for line generalization based on artificial intelligence algorithms. In Information Systems and Technologies (CISTI), 2013 8th Iberian Conference on (pp. 1-6). IEEE. [citation][year=2013]Jesus, R. D. (2013). Optimização da forma estrutural de uma barragem. [citation][year=2013]Paes, T. L. (2013). Pro-smart: predição de estruturas terciárias de proteínas utilizando sistemas multiagente. [citation][year=2012]Thyago Leite de Vasconcelos Lima (2012). Controlador Neural Com Camada Oculta Definida Por Meio De Algoritmo Genético Aplicado Ao Posicionamento De Um Manipulador Robótico. Disserrtação de Mestrado, Universidade Federal da Paraíba, Brazil, 2012. [citation][year=2011]Paulo Costa, Luis Botelho (2011). Software Image for Learning by Observation." Proceedings of 15th Portuguese Conference on Artificial Intelligence (EPIA 2011). 2011. [citation][year=2011]Joel Frederico Azevedo Costa (2011). Um ambiente gráfico para facilitar tarefas de data mining via ferramenta R. Dissertação de Mestrado em Tecnologias e Sistemas de Informação, Universidade do Minho, 2011. [citation][year=2011]J. Lopes, J. Catalão, A. Ruas (2011). Contour Line Generalization By Means Of Artificial Intelligence Techniques. Proceedings of the 25th International Cartographic Conference, 2011. [citation][year=2010]Cleyton Stang, Paulo João Martins, Priscyla Waleska Targino de Azevedo, Kristian Madeira, Merisandra Cortes Mattos (2010). Inteligência Artificial-Sistema Inteligente de Detecção de Intrusão em Redes de Computadores." Anais SULCOMP, Vol. 5, No 5, 2010. [citation][year=2009]Maria Fernanda Nogueira Gomes (2009). SMAGeB: Sistema Multi-Agente para Gestao Bancária na perspectiva da concessao de crédito. Universidade do Porto, 2009. [citation][year=2009]Telmo Machado (2009). Modelação de séries temporais–métodos lineares e não lineares. (2009), Dissertação de Mestrado em Sistemas de Informação, Instituto Politécnico de Bragrança, 2009.. [citation][year=2009]Luis da Costa Lima (2009). Apoio à Tomada de Decisão em Grupo na Área da Saúde. Universidade de Trás-os-Montes e Alto Douro, 2009. [citation][year=2008]José Nunes da Rocha (2008). Como construir um leitor de poesia, Tese de Doutoramento, FL, Universidade de Lisboa, 2008. [citation][year=2008]F. S. Gonçalves, P. W. T. A. Simões, Merisandra C. Mattos, Cristian Cechine (2008). Representação da Incerteza por Confiança em uma Shell para Modelagem da Incerteza. In: IV Congresso Sul Brasileiro de Computação (SulComp 2008), 2008, Criciuma. Anais do IV Congresso Sul Brasileiro de Computação. Criciúma / SC: UNESC, 2008. [citation][year=2008]José Pinto (2008). A* Path Planning in the CiberMouse Simulator, 2008. [citation][year=2007]António Manuel Ferrolho (2007). Integração, controlo e sequenciamento em sistemas robóticos industriais. Tese de doutoramento, DEEC, Universidade de Coimbra, 2007. [citation][year=2007]Fabrício Roulin Bittencout, Belo Horizonte (2007). Uso da Análise dos Fatores de Sensibilidade para encontrar a Quantidade Ideal Mínima de Neurônios na Camada Escondida de uma RNA Perceptron Multicamadas através dos Algoritmos Genéticos, MsC. Thesis, Universidade católica de Minas Gerais, Brazil, 2007. [citation][year=2005]Maria Fernanda Gomes, SMAGeB: sistema multi-agente para a gestão bancária na perspectiva da concessão de crédito, Tese de Mestrado, Faculdade de Economia, Universidade do Porto, 2005 1992(1 publication) [publication]Costa, E. , "New Directions for Intelligent tutoring systems,", 1992 Journal Articles 2020(1 publication) [publication]Garzon, J.A.C. and Costa, E. and Corchado, J.M. and S., J.L.J. , "Determining the maximum length of logical rules in a classifier and visual comparison of results", MethodsX, 2020 2019(3 publications) [publication]Tiago Martins and Correia, J. and Costa, E. and Penousal Machado , "Evolving Stencils for Typefaces: Combining Machine Learning, User's Preferences and Novelty", Complexity, vol. 2019, 2019 [publication]João R. Campos and Costa, E. and Marco Vieira , "Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study", IEEE Access, vol. 7, pp. 177661-177674, 2019 [publication]Macedo, J.P.G.T.d. and Marques, L. and Costa, E. , "A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective ", Sensors, 2019 2017(1 publication) [publication]Navega, D. and Cunha, E. and Costa, E. , "Lopst in the woods: the value of tree ensemble modeling for adult age-at-death estimation from skeletal degeneration", La Revue de Medicine Légale, vol. 8, pp. 181-182, 2017 2016(1 publication) [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "Unveiling the properties of structured grammatical evolution", Genetic Programming and Evolvable Machines , pp. 1-39, 2016 [citation][year=2018]Bartoli, A., Castelli, M., & Medvet, E. (2018). Weighted Hierarchical Grammatical Evolution. IEEE Transactions on Cybernetics. [citation][year=2018]Brum, A., & Ritt, M. (2018, July). Automatic Design of Heuristics for Minimizing the Makespan in Permutation Flow Shops. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. Chicago [citation][year=2018]Brum, A., & Ritt, M. (2018, April). Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 85-100). Springer, Cham. [citation][year=2018]de Souza, M., & Ritt, M. (2018, July). An Automatically Designed Recombination Heuristic for the Test-Assignment Problem. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE [citation][year=2018]de Souza, M., & Ritt, M. (2018, April). Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 67-84). Springer, Cham. [citation][year=2018]Nicolau, M., & Agapitos, A. (2018). Understanding Grammatical Evolution: Grammar Design. In Handbook of Grammatical Evolution (pp. 23-53). Springer, Cham. [citation][year=2017]Shunya Maruta and Yi Zuo and Masahiro Nagao and Eisuke Kita (2017). Grammati- cal Evolution Using Tree Representation Learning. In Neural Information Processing (pp.346-355) [citation][year=2017]Medvet, E., Daolio, F., and Tagliapietra, D. (2017). Evolvability in Grammatical Evolu- tion. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO. [citation][year=2017]Medvet, E., and Tušar, T. (2017). The DU Map: A Visualization to Gain Insights into Genotype-Phenotype Mapping and Diversity. [citation][year=2017]Nicolau, M. (2017). Understanding grammatical evolution: initialisation. Genetic Pro- gramming and Evolvable Machines, 1-41. [citation][year=2017]Klotz, D., Herrnegger, M., & Schulz, K. (2017). Symbolic regression for the estimation of transfer functions of hydrological models. Water Resources Research, 53(11), 9402-9423. [citation][year=2017]Medvet, Eric. "A Comparative Analysis of Dynamic Locality and Redundancy in Grammatical Evolution." Harvard 2014(2 publications) [publication]Simões, A. and Costa, E. , "Prediction in evolutionary algorithms for dynamic environments", Soft Computing, vol. 18, pp. 1471-1497, 2014 [citation][year=2016]Cristina Serbanescu, Cosmina Elena Pop(2016), Data analysis and statistical estimation for time series: improving presentation and interpretation. Methodologies And Application, Soft Computing, pp 1-12, Springer 2016 [citation][year=2015]Popescu, P. G., Poenaru, R., & Pop, F. (2015). New third-order Newton-like method with lower iteration number and lower TNFE. Soft Computing, 1-8. [citation][year=2015]Azevedo, C. R., & Von Zuben, F. J. (2015). Learning to Anticipate Flexible Choices in Multiple Criteria Decision-Making Under Uncertainty. [citation][year=2014]Filipiak, P., & Lipinski, P. (2014). Univariate Marginal Distribution Algorithm with Markov Chain Predictor in Continuous Dynamic Environments. In Intelligent Data Engineering and Automated Learning–IDEAL 2014 (pp. 404-411). Springer International Publishing. [citation][year=2014]Azevedo, C. R. B. (2014). Anticipation in Multiple Criteria Decision-Making Under Uncertainty. [citation][year=2014]Akandwanaho, S. M., Adewumi, A. O., & Adebiyi, A. A. (2014). Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression. Journal of Applied Mathematics, 2014. [publication]Rui L. Lopes and Costa, E. , "Developments on the Gene Regulatory Computation Device", International Journal of Natural Computing Research, vol. 3, pp. 55-91, 2014 2012(1 publication) [publication]Rui L. Lopes and Costa, E. , "The Regulatory Network Computational Device", Genetic Programming and Evolvable Machines, vol. 13, pp. 339-375, 2012 2009(1 publication) [publication]Sara Silva and Costa, E. , "Dynamic Limits for Bloat Control in Genetic Programming - and a review of past and current bloat theories", Genetic Programming and Evolvable Machines, vol. 10, pp. 141-179, 2009 [citation][year=2012]McDermott J, White DR, Luke S, Manzoni L, Castelli M, Vanneschi L, Jaskowski W, Krawiec K, Harper R, De Jong K, O'Reilly U-M (2012). Genetic Programming Needs Better Benchmarks. In Proc of Genetic and Evolutionary Computation Conference (GECCO 2012), 791-798. [citation][year=2012]Castelli M (2012). Measures and methods for robust genetic programming. PhD Thesis. University of Milano-Bicocca, Italy. [citation][year=2012]Darabos C, Giacobini M, Hu T, Moore, JH (2012). Lévy-Flight Genetic Programming: Towards a New Mutation Paradigm. European Conference on Evolutionary Computation, Machine Learning and Data Mining in Computational Biology (EvoBIO), 38-49. [citation][year=2012]Dabhi VK, Chaudhary S (2012). A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions. arXiv preprint arXiv:1211.1119. [citation][year=2012]Trujillo L, Martinez Y, Galvan-Lopez E, Legrand P (2012). A comparison of predictive measures of problem difficulty for classification with Genetic Programming. Proceedings of ERA-2012. [citation][year=2012]Ragalo AW (2012). A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming. Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), 123-129. [citation][year=2012]Trujillo L, Legrand P, Olague G, Lévy-Véhele J (2012). Evolving estimators of the pointwise Hölder exponent with Genetic Programming. Information Sciences 209: 61–79. [citation][year=2011]Fraser G, Arcuri A (2011). Evolutionary Generation of Whole Test Suites. In 11th International Conference on Quality Software (QSIC 2011), 31–40. [citation][year=2011]Fraser G, Arcuri A (2011). It is not the length that matters, it is how you control it. In 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation (ICST 2011), 150–159. [citation][year=2011]Gardner M-A, Gagné C, Parizeau M (2011). Bloat control in genetic programming with a histogram-based accept-reject method. In Proc 13th Genetic and Evolutionary Computation Conference, 187–188. [citation][year=2011]Helmuth T, Spector L, Martin B (2011). Size-based tournaments for node selection. In Proc 13th Genetic and Evolutionary Computation Conference, 799–802. [citation][year=2011]Kronberger G, Fink S, Kommenda M, Affenzeller M (2011). Macro-economic Time Series Modeling and Interaction Networks. In Proc EvoApplications 2011, 101-110. [citation][year=2011]Miller JF (2011). Cartesian Genetic Programming. Natural Computing Series. Springer. [citation][year=2011]Poli R, Salvaris M, Cinel Caterina (2011). Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control. In Genetic Programming Theory and Practice IX, 77–95. [citation][year=2011]Stokic I (2011). Primjena Genetskog Programiranja u Strojnom Ucenju. DIPLOMSKI RAD br. 213. Sveuciliste u Zagrebu, Fakultet Elektrotehnike I Racunarstva, Zagreb, Croatia. [citation][year=2011]Trujillo L (2011). Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(8): 1551–1567. [citation][year=2011]Trujillo L, Martinez Y, Melin P (2011). Estimating Classifier Performance with Genetic Programming. In Proc European Conference on Genetic Programming (EuroGP 2011), 274-285. [citation][year=2011]Trujillo L, Martinez Y, Galvan-Lopez E, Legrand P (2011). Predicting problem difficulty for genetic programming applied to data classification. In Proc Genetic and Evolutionary Computation Conference (GECCO 2011), 1355–1362. [citation][year=2011]Vanneschi L, Mussi L, Cagnoni S (2011). Hot topics in Evolutionary Computation. Intelligenza Artificiale 5(1): 5–17. [citation][year=2010]HAJIRA JABEEN, ABDUL RAUF BAIG (2010). Review of Classification Using Genetic Programming. Hajira Jabeen et al. (eds). International Journal of Engineering Science and Technology, Vol.2 (2), 2010, pp. 94-103, 2010. [citation][year=2009]Kinzett D, Johnston M, Zhang M. How Online Simplification Affects Building Blocks in Genetic Programming. In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 979"986. 2008(3 publications) [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Multidimensional Knapsack Problem: A Fitness Landscape Analysis", IEEE Transactions on Systems, Man and Cybernetics - Part B, vol. 38, pp. 604-616, 2008 [citation][year=2015]A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem JH Drake, E Özcan, EK Burke - Evolutionary computation, 2015 - MIT Press [citation][year=2015]BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY S Gupta, S Arora - International Journal of Computing, 2015 - computingonline.net [citation][year=2015]A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem MF Tasgetiren, QK Pan, D Kizilay… - … (CEC), 2015 IEEE …, 2015 - ieeexplore.ieee.org [citation][year=2015]Análise da aprendizagem de ligações em otimização evolutiva JP Martins - teses.usp.br [citation][year=2014]Multiple objective optimization and implications for single objective optimization J Gorski - 2014 - elpub.bib.uni-wuppertal.de [citation][year=2014]On the landscape of combinatorial optimization problems MH Tayarani-N, A Prugel-Bennett - … , IEEE Transactions on, 2014 - ieeexplore.ieee.org [citation][year=2014]Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem H Lu, J Liu, R Niu, Z Zhu - Soft Computing, 2014 - Springer [citation][year=2014]Fitness landscapes that depend on time H Richter - Recent Advances in the Theory and Application of …, 2014 - Springer [citation][year=2014]Solving 0–1 knapsack problem using cohort intelligence algorithm AJ Kulkarni, H Shabir - International Journal of Machine Learning and …, 2014 - Springer [citation][year=2014]On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem JP Martins, CM Fonseca, ACB Delbem - Neurocomputing, 2014 - Elsevier [citation][year=2014]A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework with MKP JH Drake, E Ozcan, EK Burke - researchgate.net [citation][year=2014]On the effectiveness of genetic algorithms for the multidimensional knapsack problem JP Martins, H Longo, ACB Delbem - Proceedings of the 2014 …, 2014 - dl.acm.org [citation][year=2014]Genetic algorithm-based testing scenarios selection for the performance evaluation of crash imminent braking systems for pedestrian safety A Gholamjafari, L Li, S Chien… - … Systems (ITSC), 2014 …, 2014 - ieeexplore.ieee.org [citation][year=2014]Zero duality gap in surrogate constraint optimization: A concise review of models B Alidaee - European Journal of Operational Research, 2014 - Elsevier [citation][year=2014]Crossover control in selection hyper-heuristics: case studies using MKP and HyFlex JH Drake - 2014 - eprints.nottingham.ac.uk [citation][year=2014]Fitness Landscapes: From Evolutionary Biology to Evolutionary Computation H Richter - Recent Advances in the Theory and Application of …, 2014 - Springer [citation][year=2013]Solving 0-1 knapsack problems based on amoeboid organism algorithm X Zhang, S Huang, Y Hu, Y Zhang… - Applied Mathematics …, 2013 - Elsevier [citation][year=2013]The travelling thief problem: the first step in the transition from theoretical problems to realistic problems MR Bonyadi, Z Michalewicz… - … (CEC), 2013 IEEE …, 2013 - ieeexplore.ieee.org [citation][year=2013]Geometricity of genetic operators for real-coded representation Y Yoon, YH Kim - Applied Mathematics and Computation, 2013 - Elsevier [citation][year=2013]A comparison of linkage-learning-based genetic algorithms in multidimensional knapsack problems JP Martins, C Bringel Neto, MK Crocomo… - … (CEC), 2013 IEEE …, 2013 - ieeexplore.ieee.org [citation][year=2013]The influence of linkage-learning in the linkage-tree GA when solving multidimensional knapsack problems JP Martins, ACB Delbem - Proceedings of the 15th annual conference …, 2013 - dl.acm.org [citation][year=2013]Predicting genetic algorithm performance on the vehicle routing problem using information theoretic landscape measures M Ventresca, B Ombuki-Berman, A Runka - 2013 - Springer [citation][year=2013]A hybrid of rough sets and genetic algorithms for solving the 0-1 multidimensional knapsack problem H Yang, M Wang, C Yang - International Journal of Innovative Computing, …, 2013 - ijicic.org [citation][year=2013]Dynamic fitness landscape analysis H Richter - Evolutionary Computation for Dynamic Optimization …, 2013 - Springer [citation][year=2013]Analysis of the fitness landscape for the class of combinatorial optimisation problems MH Tayarani-Najaran - 2013 - eprints.soton.ac.uk [citation][year=2013]A Study of Representations for Resource Constrained Project Scheduling Problems Using Fitness Distance Correlation B Cai, J Liu - Intelligent Data Engineering and Automated Learning– …, 2013 - Springer [citation][year=2013]Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms M Crossley, A Nisbet, M Amos - Adaptive and Natural Computing …, 2013 - Springer [citation][year=2013]Clustering of search trajectory and its application to parameter tuning HC Lau, D Lo - Journal of the Operational Research …, 2013 - palgrave-journals.com [citation][year=2012]Kate Smith-Miles, Leo Lopes, Measuring instance difficulty for combinatorial optimization problems, Computers & Operations Research, Volume 39, Issue 5, May 2012, Pages 875-889, ISSN 0305-0548, 10.1016/j.cor.2011.07.006. [citation][year=2012]J Gorski, L Paquete, F Pedrosa. Greedy algorithms for a class of knapsack problems with binary weights. Computers & Operations Research, 2012 - Elsevier. [citation][year=2012]J Liu, HA Abbass, DG Green, W Zhong. Motif difficulty (MD): a predictive measure of problem difficulty for evolutionary algorithms using network motifs. Evolutionary Computation, 2012 - MIT Press [citation][year=2012]Z Ren, H Jiang, J Xuan, Z Luo. An Accelerated-Limit-Crossing-Based Multilevel Algorithm for the p-Median Problem. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012. [citation][year=2012]E Pitzer, M Affenzeller. A comprehensive survey on fitness landscape analysis. Recent Advances in Intelligent Engineering Systems, Studies in Computational Intelligence, 2012 - Springer. [citation][year=2012]M Crossley, A Nisbet, M Amos. Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms. arXiv:1210.3210, 2012. [citation][year=2011]H. Bouziri, K. Mellouli, E. Talbi (2011). The K-coloring fitness landscape. Journal of Combinatorial Optimization, 21, 3, 306-329. [citation][year=2011]Jochen Gorski, Luís Paquete, Fábio Pedrosa, Greedy algorithms for a class of knapsack problems with binary weights, Computers & Operations Research, Volume 39, Issue 3, March 2012, Pages 498-511, ISSN 0305-0548, 10.1016/j.cor.2011.02.010. [citation][year=2011]Moritz, R.; Ulrich, T.; Thiele, J.L.; Buerklen, S.; , "Mutation operator characterization: Exhaustiveness, locality, and bias," Evolutionary Computation (CEC), 2011 IEEE Congress on , vol., no., pp.1396-1403, 5-8 June 2011 [citation][year=2011]Jing Liu, Hussein A. Abbass, David G. Green, Weicai Zhong, "Motif Difficulty (MD): A Predictive Measure of Problem Difficulty for Evolutionary Algorithms Using Network Motif", Evolutionary Computation Journal, Posted Online August 4, 2011.(doi:10.1162/EVCO_a_00045) [citation][year=2011]Pitzer, Erik, and Affenzeller, Michael, "A Comprehensive Survey on Fitness Landscape Analysis", Recent Advances in Intelligent Engineering Systems, Studies in Computational Intelligence, 2012, Springer Berlin / Heidelberg, Isbn: 978-3-642-23228-2, pp. 161-191, Volume: 378, Doi: 10.1007/978-3-642-23229-9_8 [citation][year=2011]Tayarani N., M. H. et. al, "Improvement of the Performance of QEA Using the History of Search Process and Backbone Structure of Landscape", Innovative Computing Technology, Communications in Computer and Information Science, 2011, Springer Berlin Heidelberg, Isbn: 978-3-642-27337-7, pp. 389-400, Volume: 241, Doi: 10.1007/978-3-642-27337-7_37 [citation][year=2010]Jeff Riley and Vic Ciesielski (2010). Fitness Landscape Analysis for Evolutionary Non-Photorealistic Rendering. WCCI 2010 IEEE World Congress on Computational Intelligence - IEEE CEC, pp. 2537-2545, July, 18-23, 2010 - CCIB, Barcelona, Spain, IEEE, 2010. [citation][year=2010]M Caserta, S Voß. Metaheuristics: intelligent problem solving. Matheuristics, 2010 - Springer. [citation][year=2010]J Gorski . Multiple objective optimization and implications for single objective optimization. PhD. thesis, 2010. [citation][year=2009]Uludag, G., and Uyar, A.S., "Fitness landscape analysis of differential evolution algorithms", Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2-4 Sept. 2009 [citation][year=2009]Minh Nghia Le, Yew-Soon Ong, Yaochu Jin, and Bernhard Sendhoff, extbf{Lamarckian memetic algorithms: local optimum and connectivity structure analysis}, Memetic Computing Journal, Vol. 1, No. 3, pp. 175-190, 2009 [citation][year=2009]M. Caserta and S. VoÃ?, extbf{Metaheuristics: Intelligent problem solving}, Chapter in: V. Maniezzo, T. Stutzle and S. VoÃ? (eds.) Matheuristics: Hybridizing Metaheuristics and Mathematical Programming, Book Series "Annals of Information Systems,â? 10, Springer, Berlin, 2009. [citation][year=2009]Pereira, Paulo Alexandre da Silva, exbf{An Optimization-Based Decision Support System for Planning Self-Promotion of a Television Station}, PhD. Thesis, School of Sciences, University of Minho, May, 2009. [citation][year=2009]H. Bouziri, K. Mellouli, El-G. Talbi (2009). The k-coloring Fitness Landscape. Journal of Combinatorial Optimization. [publication]Menezes, T. and Costa, E. , "Modeling Evolvable Brains - An Heterogeneous Network Approach", International Journal of Information Technology and Inteligent Computing, vol. 2, 2008 [publication]Tiago Baptista and Costa, E. , "Evolution of a multi-agent system in a cyclical environment", Theory in Biosciences, vol. 127, pp. 141-148, 2008 [citation][year=2014]Barreto, Nuno, Luis Macedo, and Licinio Roque. "Multiagent System Architecture in Orphibs II." ALIFE 14: The Fourteenth Conference on the Synthesis and Simulation of Living Systems. Vol. 14. 2005(2 publications) [publication]Brabazon, A. and Silva, A. and Sousa, T. and O'Neill, M. and Matthews, R. and Costa, E. , "Organizational strategic adaptation in the presence of inertia", Advances in Complex Systems, vol. 8, pp. 497-520, 2005 [citation][year=2009]L. Chasalow (2009). A MODEL OF ORGANIZATIONAL COMPETENCIES FOR BUSINESS INTELLIGENCE SUCCESS. PhD Thesis, Virginia Commonwealth University, 2009. [citation][year=2009]E Make (2009). The Emergence of a Market: What Efforts Can Entrepreneurs Make?. Natural Computing in Computational Finance 2, 2009 [citation][year=2008]G Easton, RJ Brooks, K Georgieva (2008). Understanding the Dynamics of Industrial Networks Using Kauffman Boolean Networks. Advances in Complex Systems, Vol. 11, No. 1, pp. 139"164, World Scientific Press, 2008. [publication]Brabazon, A. and Silva, A. and Sousa, T. and O'Neill, M. and Matthews, R. and Costa, E. , "Investigating Strategic Inertia Using OrgSwarm", Informatica, pp. 125-141, 2005 [citation][year=2010]Alec Banks, Jonathan Vincent (2010). Hybridisation of particle swarm optimisation with area concentrated search.International Journal of Knowledge-Based and Intelligent Engineering Systems, Volume 14, Number 2, pp. 95-114, IOS Press, 2010. [citation][year=2010]Alan Godoy Souza Mello (2010). Aplicação de Redes Complexas para a Definição de Vizinhança na Otimização por Enxame de Partículas. Tese de Mestrado, Universidade Estadual de Campinas, 2010. [citation][year=2008]Alec Banks, Jonathan Vincent and Chukwudi Anyakoha (2008). A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing, Computer Science, Volume 7, Number 1, pp. 109-124, Springer 2008. [citation][year=2007]Alec Banks , Jonathan Vincent and Chukwudi Anyakoha, A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Publisher Springer Netherlands, 2007. [citation][year=2007]Xiaohui Cui and Thomas E. Potok (2007): A Particle Swarm Social Model for Multi-Agent Based Insurgency Warfare Simulation. Fifth International Conference on Software Engineering Research, Management and Applications. pp. 177-183, IEEE Press, 2007. 2004(1 publication) [publication]Sousa, T. and Silva, A.P.N.F.d. and Silva, A. and Costa, E. , "Particle Swarm Based Data Mining Algorithms for Classification Tasks", Parallel Computing, pp. 767-783, 2004 [citation][year=2010]Hongbo Liu, Ajith Abraham and Benxian Yue (2010). Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction. Advances in Machine Learning II, Studies in Computational Intelligence, Volume 263, pp. 445-466, Springer 2010. [citation][year=2010]Suraj Pandey, Linlin Wu, Siddeswara Guru, and Rajkumar Buyya (2010). A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments, Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA 2010), Perth, Australia, April 20-23, 2010 [citation][year=2010]Adham Atyabi, Somnuk Phon-Amnuaisuk, Chin Kuan Ho (2010). Applying Area Extension PSO in Robotic Swarm. Journal of Intelligent & Robotic Systems, Volume 58, Numbers 3-4, pp. 253-285, Springer 2010. [citation][year=2009]A. B. S. Serapião and J. R. P. Mendes (2009). Classification of Petroleum Well Drilling Operations with a Hybrid Particle Swarm/Ant Colony Algorithm. Next-Generation Applied Intelligence, Lecture Notes in Computer Science, 5579, pp. 301-310, Springer 2009. [citation][year=2009]A. Cervantes, I. M. Galván, P. Isasi (2009).AMPSO: A new Particle Swarm Method for Nearest Neighborhood Classification. IEEE Transactions on Systems, Man, and Cybernetics: Part B, vol. 39, n. 5, Oct. 2009, p. 1082 - 1091, IEEE Press, 2009. [citation][year=2009]A. Serapião (2009). Fundamentos de otimização por inteligência de enxames: uma visão geral, Sba: Controle & Automação Sociedade Brasileira de Automatica, vol.20 n.3, Scielo, 2009. [citation][year=2009]Adham Atyabi, Somnuk Phon-Amnuaisuk, Chin Kuan Ho (2009). Applying Area Extension PSO in Robotic Swarm. Journal of Intelligent and Robotic Systems , pp. 1-33, Springer Netherlands, 2009. [citation][year=2009]Ajith Abraham and Hongbo Liu (2009). Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning. Foundations of Computational Intelligence, Studies in Computational Intelligence, Volume 3, pp. 291-312, Springer 2009. [citation][year=2009]Alejandro Cervantes, Inés Galván and Pedro Isasi (2009). Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems. New Generation Computing, Vol 27, Num 3, pp. 239-257, Springer 2009. [citation][year=2009]Augusto de Almeida Prado G. Torácio (2009). Multiobjective Particle Swarm Optimization in Classification-Rule Learning. Swarm Intelligence for Multi-objective Problems in Data Mining, Studies in Computational Intelligence, Volume 242, pp. 37-64, Springer, 2009. [citation][year=2009]Bashir Mohammed Ghandi (2009). Classification of Facial Emotions using Guided Particle Swarm Optimization I. International Journal on Computer and Communication Technology , Vol. 1, No. 1, 2009. [citation][year=2009]Bilal Alatas and Erhan Akina(2009). Multi-objective rule mining using a chaotic particle swarm optimization algorithm. Knowledge-Based Systems Volume 22, Issue 6, August 2009, Pages 455-460, Elsevier 2009. [citation][year=2009]Chih-Chuan Chen, Chao-Chin Hsu, Yi-Chung Cheng, Sheng-Tun Li (2009). Knowledge Discovery on In Vitro Fertilization Clinical Data Using Particle Swarm Optimization. 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering, pp.278-283, 2009. [citation][year=2009]Chih-Chuan Chen, Chao-Chin Hsu, Yi-Chung Cheng, Sheng-Tun Li and Ying-Fang Chan (2009).Comprehensible Knowledge Discovery Using Particle Swarm Optimization with Monotonicity Constraints. Opportunities and Challenges for Next-Generation Applied Intelligence, Studies in Computational Intelligence, Volume 214/2009, pp. 323-328, Springer 2009. [citation][year=2009]H Liu, A Abraham (2009). Chaos and Swarm, Intelligent Computing Based on Chaos, Springer, 2009. [citation][year=2009]Hongbo Liu and Ajith Abraham6 (2009). Chaos and Swarm Intelligence. Intelligent Computing Based on Chaos, Studies in Computational Intelligence, Volume 184/2009, pp. 197-212, Springer 2009. [citation][year=2009]Hongbo Liu, Ajith Abraham and Yanheng Li (2009). Nature Inspired Population-Based Heuristics for Rough Set Reduction. Rough Set Theory: A True Landmark in Data Analysis, Studies in Computational Intelligence, Volume 174, 261-278, Springer 2009. [citation][year=2009]Mingyan Zhao, Hongbo Liu, Ajith Abraham, Emilio Corchado (2009). A Swarm-Based Rough Set Approach for Group Decision Support Systems. 2009 Ninth International Conference on Hybrid Intelligent Systems, vol. 3, pp.365-369, 2009. [citation][year=2009]S. Dehuri, S.Ghosh and C. A. Coello Coello (2009). An Introduction to Swarm Intelligence for Multi-objective Problems. Swarm Intelligence for Multi-objective Problems in Data Mining, Studies in Computational Intelligence, Volume 242, pp. 1-17, Springer 2009. [citation][year=2009]Seyed-Hamid Zahiri, Seyed-Alireza Seyedin (2009). Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers. Swarm Intelligence for Multi-objective Problems in Data Mining, Book Series Studies in Computational Intelligence, Volume 242, pp. 65-92, Springer 2009. [citation][year=2009]Shao-Rong Huang (2009). Survey of particle swarm optimization algorithm. Computer Engineering and Design. Vol. 30, no. 8, pp. 1977-1980. 2009. [citation][year=2009]Shelly Bansal, Daya Gupta, V. K. Panchal and Shashi Kumar (2009). Swarm Intelligence Inspired Classifiers in Comparison with Fuzzy and Rough Classifiers: A Remote Sensing Approach. Contemporary Computing, Communications in Computer and Information Science, Volume 40, pp. 284-294, Springer 2009. [citation][year=2009]Suraj Pandey, Linlin Wu, Siddeswara Guru, Rajkumar Buyya (2009). A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. NET-based Cloud Computing, Technical Report, CLOUDS-TR-2009. [citation][year=2009]TH Sun (2009). Applying particle swarm optimization algorithm to roundness measurement, Expert Systems With Applications, Elsevier, 2009. [citation][year=2009]WC Yeh (2009). A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems, Expert Systems with Applications, Elsevier, 2009. [citation][year=2009]WC Yeh, WW Chang, YY Chung (2009). A new hybrid approach for mining breast cancer pattern using discrete particle particle swarm optimization and statistical methods. Expert Systems with Applications, Elsevier, 2009. [citation][year=2009]Leandro dos Santos Coelho (2009), Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems, Expert Systems with Applications, Volume 37, Issue 2, March 2010, Pages 1676-1683, ISSN 0957-4174, DOI: 10.1016/j.eswa.2009.06.044. [citation][year=2009]C. Dalian (2009). Nature Inspired Population-Based Heuristics for Rough Set Reduction. Rough Set Theory: A True Landmark in Data Analysis, 2009. [citation][year=2009]Haijun Su, Yupu Yang, Liang Zhao, Classification rule discovery with DE/QDE algorithm, Expert Systems with Applications, Volume 37, Issue 2, March 2010, Pages 1216-1222, Elsevier, 2009. [citation][year=2009]C Nalini, PB Balasubramanie (2009). Performance Analysis of Cooperative PSO Algorithm with ACO and Tabu Search, International Journal of Computational Intelligence, Volume 5, Number 2 (2009). [citation][year=2008]Y Zheng, Y Meng (2008), OBJECT DETECTION AND TRACKING USING BAYES-CONSTRAINED PARTICLE SWARM OPTIMIZATION, Computer Vision Research Progress, 2008 - Nova Science Publishers [citation][year=2008]N. O. S. Ba-Karait, S. Mariyam Shamsuddin (2008). Handwritten Digits Recognition Using Particle Swarm Optimization . Second Asia International Conference on Modelling & Simulation, pp. 615-619, IEEE. [citation][year=2008]A. Toracio (2008). Aprendizado de regras de classificação com otimização por nuvem de particulas multiobjetivo . Master Thesis, 2008. [citation][year=2008]Abraham, A. Hongbo Liu (2008). Swarm intelligence based rough set reduction scheme for support vector machines. Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 200-202, IEEE Press, 2008. [citation][year=2008]Nicholas Holden and Alex A. Freitas (2008). A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining. Journal of Artificial Evolution and Applications, Volume 2008, Hindawi Publishing , 2008. [citation][year=2008]Neveen Ibrahim Ghali, Nahed Eldesouky, Mervat A. Nabyand Lamiaa Bakrawy (2008). IMPROVEMENT OF DATA CLUSTERING USING PARTICLE SWARM OPTIMIZATION, Far East Journal of Electronics and Communications, Volume 2, Issue 2, Pages 121 - 132, 2008. [citation][year=2008]Yanchao Yin, Linfu Sun, Min Han (2008). A High-Accuracy Parameter Estimation PSO Algorithm. International Conference on Embedded Software and Systems Symposia, pp. 7-12, IEEE Press, 2008. [citation][year=2008]Wei-Chang Yeh, and Sin-Long Liu (2008). A discrete particle swarm optimization for evaluating the multiple multi-level redundancy allocation problem. 5th International Conference on Information Technology and Applications, ICITA 2008, pp. 408-413, 2008. [citation][year=2007]De Falco, A Della Cioppa, E Tarantino - Facing classification problems with Particle Swarm Optimization. Applied Soft Computing, 2007 ¨C Elsevier. [citation][year=2007]H Liu, A Abraham, M Clerc - Chaotic dynamic characteristics in swarm intelligence. Applied Soft Computing, 2007 ¨C Elsevier. [citation][year=2007]Nicholas Paul Holden, Alex A. Freitas. A hybrid PSO/ACO algorithm for classification. Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, London, United Kingdom. WORKSHOP SESSION: Particle swarms the second decade, pp. 2745-2750, ISBN:978-1-59593-698-1, ACM Press 2007. [citation][year=2007]H Liu, A Abraham, W Zhang, A fuzzy adaptive turbulent particle swarm optimization, International Journal of Innovative Computing and Applications, Volume 1, Number 1, pp. 39 ¨C 47, 2007. [citation][year=2007]Benxian Yue, Weihong Yao , Ajith Abraham, Hongbo Liu1 (2007): A New Rough Set Reduct Algorithm Based on Particle Swarm Optimization. Bio-inspired Modeling of Cognitive Tasks, LNCS 4527, pp. 397-406, Springer, 2007. [citation][year=2007]Cui Zhi-hua, Zeng Jian-chao, Sun Guo-ji (2007): Adaptive Integral-controller Particle Swarm Optimization Using Accelerator Feedback. Journal of Chinese Computer Systems, vol.28, No.5, pp. 855-860, 2007. [citation][year=2007]Eduardo P. Costa , Ana C. Lorena , André C. P. L. F. Carvalho , Alex A. Freitas and Nicholas Holden (2007): Comparing Several Approaches for Hierarchical Classification of Proteins with Decision Trees. Advances in Bioinformatics and Computational Biology, LNCS 4643, pp.126-137, Springer, 2007. [citation][year=2007]Inthachot, Montri Supratid, Siriporn (2007): A Multi-Subpopulation Particle Swarm Optimization: A Hybrid Intelligent Computing for Function Optimization. In Natural Computation, 2007, vol 5, pp. 679-684, IEEE Press. [citation][year=2007]Lei, Wang; Qi, Kang; Hui, Xiao; Qidi, Wu, Traffic Intelligent Optimization and Local Traffic-Flow Control Inside Shanghai-EXPO-Area, Networking, Sensing and Control, 2007 IEEE International Conference on. Volume , Issue , 15-17 April 2007 Page(s):856 " 861 [citation][year=2007]Cervantes, A.; Galvan, I.; Isasi, P., Building Nearest Prototype Classifiers Using a Michigan Approach PSO, Swarm Intelligence Symposium, 2007. SIS 2007. IEEE. Volume , Issue , 1-5 April 2007 Page(s):135 - 140 [citation][year=2007]A de Almeida, PG Toracio, A Trinicad Ramirez Pozo (2007), Multiple objective particle swarm for classification-rule discovery, Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007. [citation][year=2007]Liang-Chi Wang (2007). Classification Rule Discovery with Particle Swarm Optimization. Master Thesis, 2007. [citation][year=2006]N Holden, AA Freitas, Hierarchical Classification of G-Protein-Coupled Receptors with a PSO/ACO Algorithm, Proc. IEEE Swarm Intelligence Symposium (SIS-2006), pp. 77-84. [citation][year=2006]Crina Grosan, Ajith Abraham and Monica Chis: Swarm Intelligence in Data Mining, Studies in Computational Intelligence (SCI) 34, 1"20 (2006). [citation][year=2006]De Falco, I. Cioppa, A. D. Tarantino, E., Evaluation of Particle Swarm Optimization Effectiveness in Classification, Lecture Notes in Computer Science, 2006, Vol. 3849, pages 164-171., Springer-Verlag. [citation][year=2006]Cui, Z. Cai, X. Zeng, J. Sun, G., Predicted-Velocity Particle Swarm Optimization Using Game-Theoretic Approach, Lecture Notes in Computer Science, 2006, Vol. 4115, pages 145-154, Springer-Verlag. [citation][year=2006]Qi Kang, Lei Wang, Qi-di Wu, Research on Fuzzy Adaptive Optimization Strategy of Particle Swarm Algorithm, International Journal of Information Technology, Vol.12, No.3, 2006. [citation][year=2006]Wang Lei  Kang Qi  Wu Qi-Di, Multi-optimum Fuzzy Programming Based Particle Swarm Optimization, Control And Decision, 2006 Vol.21 No.6 P.680-684 [citation][year=2006]Liu Hong-Bo  Wang Xiu-Kun  Tan Guo-Zhen, Convergence Analysis of Particle Swarm Optimization and Its Improved Algorithm Based on Chaos, Control And Decision, 2006 Vol.21 No.6 P.636-640,645 [citation][year=2006]A. Abraham, He Guo, and Hongbo Liu, "Swarm Intelligence: Foundations, Perspectives and Applications", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), approx. 300 pages (hardcover), Springer, Germany, 2006. [citation][year=2005]Nicholas Holden, Alex A. Freitas, A Hybrid Particle Swarm/Ant Colony Algorithm for the Classification of Hierarchical Biological Data, 2005 IEEE Swarm Intelligence Symposium, 8-10 June, Pasadena, California, USA. [citation][year=2005]Wang Lei, Kang Qi, Xiao Hui, Wu Qidi, "A Modified Adaptive Particle Swarm Optimization Algorithm?, ICIT 2005. IEEE International Conference on Industrial Technology, 14-17 Dec. 2005. [citation][year=2005]Cervantes, A. Galvan, I. Isasi, P., "A Comparison between the Pittsburgh and Michigan Approaches for the Binary PSO Algorithm?, the 2005 IEEE Congress on Evolutionary Computation, 02-05 Sept. 2005. [citation][year=2005]Andries P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, ISBN: 0-470-09191-6, November 2005, John Wiley & Sons, Ltd. [citation][year=2005]KANG Qi, WANG Lei, WU Qi-di, "Fuzzy Adaptive Programming Algorithm Based on Particle Swarm Multi-optimum Information?, INFORMATION AND CONTROL, 2005 Vol.34 No.4 P.439-443,450 1994(1 publication) [publication]Nedellec, C. and Correia, J. and Ferreira, J.L. and Ferreira, J.L. and Costa, E. , "Machine Learning Goes to the Bank", Applied Artificial Intelligence, 1994 [citation][year=1999]G I Webb, J Wells and Z J Zheng, An experimental evaluation of integrating machine learning with knowledge acquisition, Machine Learning, 35 (1): 5-23, April 1999. [citation][year=1996]G.I. Webb and J. Wells. Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition Through Direct Interaction with Domain Experts. In Proceedings PKAW '96: Pacific Knowledge Acquisition Workshop, 1996. [citation][year=1994]Y. Kodratoff, Industrial Applications of ML: Illustrations for the KAML Dilemma and the CBR Dream, in Proceedings od the European Conference on Machine Learning, L. Raedt and F. Bergadano (Eds.), LNAI, vol. 784, Springer-Verlag (1994), pp 3-19. 1992(1 publication) [publication]Costa, E. and Amilcar Cardoso , "Consistency-Checking Along Time", Applied Artificial Intelligence, vol. 6, pp. 207-247, 1992 1988(1 publication) [publication]Coelho, H. and Viccari, R. and Costa, E. , "Pragmatic attachment devices for conversations with tutors", Applied Artificial Intelligence, vol. 2, pp. 277-284, 1988 1987(2 publications) [publication]Costa, E. , "Ensino Assistido por Computador e Inteligência Artificial", Revista de Informática, vol. 6, pp. 37-43, 1987 [publication]Duchénoy, S. and Costa, E. , "A method for the discovering and explanation of students' misconceptions", Modelling, Simulation and Control, vol. 9, pp. 43-54, 1987 1983(1 publication) [publication]Costa, E. , "Nota sobre o problema da generalização", Psicologia, vol. 4, pp. 57-60, 1983 Conference Articles 2020(2 publications) [publication]Macedo, J.P.G.T.d. and Marques, L. and Costa, E. , "Locating Odour Sources with Geometric Syntactic Genetic Programming", in Applications of Evolutionary Computation, 2020 [publication]João R. Campos and Costa, E. and Marco Vieira , "On Configuring a Testbed for Dependability Experiments: Guidelines and Fault Injection Case Study", in Computer Safety, Reliability, and Security, 2020 2019(2 publications) [publication]João R. Campos and Marco Vieira and Costa, E. , "Propheticus: Machine Learning Framework for the Development of Predictive Models for Reliable and Secure Software", in International Symposium on Software Reliability Engineering (ISSRE), 2019 [publication]Macedo, J.P.G.T.d. and Marques, L. and Costa, E. , "A performance comparison of bio-inspired behaviours for odour source localisation", in 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2019 2018(3 publications) [publication]Macedo, J.P.G.T.d. and Fonseca, C.M. and Costa, E. , "Geometric crossover in syntactic space", in Genetic Programming, 21st European Conference, EuroGP 2018, Proceedings, vol. 10781 of Lecture Notes in Computer Science, pp. 237-252, 2018 [publication]João R. Campos and Marco Vieira and Costa, E. , "Exploratory Study of Machine Learning Techniques for Supporting Failure Prediction", in 2018 14th European Dependable Computing Conference (EDCC), 2018 [publication]Tiago Martins and Correia, J. and Costa, E. and Penousal Machado , "Evotype: Towards the Evolution of Type Stencils", in Proceedings of 7th International Conference of Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, 2018 2017(3 publications) [publication]Lourenço, Nuno and Ferrer, J. and Pereira, F.B. and Costa, E. , "A Comparative Study of Different Grammar-Based Genetic Programming Approaches", in EuroGP, 2017 [citation][year=2018]Bartoli, A., Castelli, M., & Medvet, E. (2018). Weighted Hierarchical Grammatical Evolution. IEEE Transactions on Cybernetics. [citation][year=2018]Nicolau, M., & Agapitos, A. (2018). Understanding Grammatical Evolution: Grammar Design. In Handbook of Grammatical Evolution (pp. 23-53). Springer, Cham. Chicago [citation][year=2017]Nicolau, M. (2017). Understanding grammatical evolution: initialisation. Genetic Pro- gramming and Evolvable Machines, 1-41. [publication]Macedo, J.P.G.T.d. and Marques, L. and Costa, E. , "Robotic odour search: Evolving a robot's brain with Genetic Programming", in 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2017 [publication]João R. Campos and Pereira, F.B. and Costa, E. , "Extracting Knowledge from Data to Predict Mitochondrial Toxicity Indexes for Pharmacological Compounds (Poster Presentation)", in MitoPorto International Symposium, Porto, Portugal, 2017, 2017 2016(5 publications) [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "An Evolutionary Approach to the Full Optimization of the Traveling Thief Problem", in EvoCOP, 2016 [citation][year=2018]Wu, J., Polyakovskiy, S., Wagner, M., & Neumann, F. (2018). Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem. arXiv preprint arXiv:1802.02434. [citation][year=2018]Nieto-Fuentes, R., Segura, C., & Valdez, S. I. (2018, July). A Guided Local Search Approach for the Travelling Thief Problem. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. [citation][year=2018]Wuijts, R. H. (2018). Investigation of the Traveling Thief Problem (Master's thesis). [citation][year=2017]Vieira, D. K., Soares, G. L., Vasconcelos, J. A., and Mendes, M. H. (2017, April). A Ge- netic Algorithm for Multi-component Optimization Problems: The Case of the Travel- ling Thief Problem. In European Conference on Evolutionary Computation in Combi- natorial Optimization (pp. 18-29). Springer, Cham. [citation][year=2017]Moeini,M.,Schermer,D.,andWendt,O.(2017,July).AHybridEvolutionaryApproach for Solving the Traveling Thief Problem. In International Conference on Computational Science and Its Applications (pp. 652-668). Springer, Cham. [citation][year=2017]Polyakovskiy, Sergey, and Frank Neumann. "The Packing While Traveling Problem." European Journal of Operational Research 258, no. 2 (2017): 424-439. [publication]Macedo, J.P.G.T.d. and Costa, E. , "Genetic Programming Algorithms for Dynamic Environments", in 19th European Conference, EvoApplications 2016, 2016 [publication]Macedo, J.P.G.T.d. and Costa, E. , "Evolving Neural Networks for Multi Robot Odor Search", in IEEE International Conference on Autonomous Robot Systems and Competitions, 2016 [publication]Tiago Martins and Correia, J. and Costa, E. and Penousal Machado , "Evotype: From Shapes to Glyphs", in Genetic and Evolutionary Computation Conference (GECCO), 2016 [publication]Ana Rodrigues and Costa, E. and Amilcar Cardoso and Penousal Machado and Tiago Cruz , "Evolving L-Systems with Musical Notes", in Evolutionary and Biologically Inspired Music, Sound, Art and Design: 5th International Conference, EvoMUSART 2016, Proceedings, 2016 [citation][year=2018]Cao, Xizheng & Zhan, Wen. (2018). Intelligent composition method for the prairie-song melodies of northern China. Journal of Intelligent & Fuzzy Systems. 1-15. DOI: 10.3233/JIFS-18920. [citation][year=2017]ALVES DA VEIGA, Pedro. Generative Theatre of Totality. Journal of Science and Technology of the Arts, [S.l.], v. 9, n. 3, p. 33-43, dec. 2017. ISSN 2183-0088. Available at: . Date accessed: 26 Dec. 2017. doi:http://dx.doi.org/10.7559/citarj.v9i3.422. 2015(4 publications) [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "The Optimization Ability of Evolved Strategies", in 17th Portuguese Conference on Artificial Intelligence (EPIA 2015), 2015 [citation][year=2017]Soria-Alcaraz, J. A., Espinal, A., and Sotelo-Figueroa, M. A. (2017). Evolvability metric estimation by a parallel perceptron for on-line selection hyper-heuristics. IEEE Access, 5, 7055-7063. [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "A grammatical evolution hyper-heuristic for the integration and test order problem." In GECCO. ACM, 2016. [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "Grammatical Evolution for the Multi-Objective Integration and Test Order Problem." In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 1069-1076. ACM, 2016. [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "Automatic Design of Algorithms Applied to the Multi-Objective TSP Problem." [publication]Tiago Martins and Correia, J. and Costa, E. and Penousal Machado , "Evotype: Evolutionary Type Design", in Colin Johnson and Adrian Carballal and João Correia editors , Proceedings of the 4th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2015, Copenhagen, Denmark, April 8-10, 2015, 2015 [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "SGE: A Structured Representation for Grammatical Evolution", in Artificial Evolution 2015, 2015 [citation][year=2018]Medvet, E., Bartoli, A., De Lorenzo, A., & Tarlao, F. (2018). Designing automatically a representation for grammatical evolution. Genetic Programming and Evolvable Machines, 1-29. [citation][year=2018]Medvet, E., Bartoli, A., De Lorenzo, A., & Tarlao, F. (2018, September). GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution. In International Conference on Parallel Problem Solving from Nature (pp. 223-235). Springer, Cham. [citation][year=2018]Bartoli, A., Castelli, M., & Medvet, E. (2018). Weighted Hierarchical Grammatical Evolution. IEEE Transactions on Cybernetics. [citation][year=2018]Ryan, C., O’Neill, M., & Collins, J. J. (2018). Introduction to 20 Years of Grammatical Evolution. In Handbook of Grammatical Evolution (pp. 1-21). Springer, Cham. [citation][year=2018]Medvet, E., & Bartoli, A. (2018, April). On the Automatic Design of a Representation for Grammar-Based Genetic Programming. In European Conference on Genetic Programming (pp. 101-117). Springer, Cham. [citation][year=2018]Fagan, D., & Murphy, E. (2018). Mapping in Grammatical Evolution. In Handbook of Grammatical Evolution (pp. 79-108). Springer, Cham. [citation][year=2018]Medvet, E., Virgolin, M., Castelli, M., Bosman, P. A., Gonçalves, I., & Tušar, T. (2018). Unveiling evolutionary algorithm representation with DU maps. Genetic Programming and Evolvable Machines, 19(3), 351-389. [citation][year=2017]Medvet, E. (2017). Hierarchical Grammatical Evolution. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO. [citation][year=2017]Medvet, E., Daolio, F., and Tagliapietra, D. (2017). Evolvability in Grammatical Evolu- tion. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO. [citation][year=2017]Medvet, E., and Tušar, T. (2017). The DU Map: A Visualization to Gain Insights into Genotype-Phenotype Mapping and Diversity. [citation][year=2017]Ryser-Welch, P. (2017). Evolving comprehensible and scalable solvers using CGP for solving some real-world inspired problems (Doctoral dissertation, University of York). [citation][year=2017]Medvet, Eric, Alberto Bartoli, and Jacopo Talamini. "Road Traffic Rules Synthesis using Grammatical Evolution." [citation][year=2017]Medvet, Eric. "A Comparative Analysis of Dynamic Locality and Redundancy in Grammatical Evolution." [publication]Vanneschi, L. and Castelli, M. and Costa, E. and Vaz, H. and Lobo, V. and Urbano, P. , "Improving maritime awareness with semantic genetic programming and linear scaling: prediction of vessels positions based on AIS data", in EvoAPPS, 2015 2014(3 publications) [publication]Leonor Melo and Pereira, F.B. and Costa, E. , "Effective Multi-caste Ant Colony System for Large Dynamic Traveling Salesperson Problems", in Artificial Evolution (EA-2013), 2014 [publication]Leonor Melo and Pereira, F.B. and Costa, E. , "Extended experiments with Ant Colony Optimization with heterogeneous ants for Large Dynamic Traveling Salesperson Problems", in ICCSA14: The 14th International Conference on Computational Science and Its Applications, 2014 [citation][year=2018]Mavrovouniotis, Michalis; Shengxiang Yang, : Ant colony optimization for dynamic combinatorial optimization problems (Control, Robotics & Sensors, 2018), 'Swarm Intelligence - Volume 1: Principles, current algorithms and methods', Chap. 5, pp. 121-142, DOI: 10.1049/PBCE119F_ch5. [citation][year=2018]M. Mavrovouniotis, S. Yang, Ant Colony Optimization for Dynamic Combinatorial Optimization Problems, Swarm Intelligence: From Concepts to Applications, chapter 5, Publisher The IET, 2018 [citation][year=2017]A. Fayeez, E. Keedwell, M. Collett. H-ACO: A Heterogeneous Ant Colony Optimisation approach with Application to the Travelling Salesman Problem, Proceeding of EA 2017. [citation][year=2017]M. Mavrovouniotis, C. Li, S. Yang, A survey of swarm intelligence for dynamic optimization: Algorithms and applications, Swarm and Evolutionary Computation 33:1-17, April 2017 [publication]Tiago Baptista and Costa, E. , "Automatic Step Evolution", in 14th edition of the Ibero-American Conference on Artificial Intelligence, IBERAMIA 2014, 2014 2013(9 publications) [publication]Tiago Baptista and Costa, E. , "Step Evolution: Improving the Performance of Open-Ended Evolution Simulations", in IEEE ALIFE 2013, The 2013 IEEE Symposium on Artificial Life, 2013, 2013 [publication]Leonor Melo and Pereira, F.B. and Costa, E. , "Multi-caste ant colony algorithm for the dynamic traveling salesperson problem", in M. Tomassini et al. (Eds.): ICANNGA 2013, LNCS 7824, pp. 226--235. Springer, Heidelberg (2013), April 2013, 2013 [citation][year=2019]Oliveira S., Wanner E.F., de Souza S.R., Bezerra L.C.T., Stützle T. (2019) The Hypervolume Indicator as a Performance Measure in Dynamic Optimization. In: Deb K. et al. (eds) Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science, vol 11411. Springer, Cham [citation][year=2018]Mavrovouniotis, Michalis; Shengxiang Yang, : Ant colony optimization for dynamic combinatorial optimization problems (Control, Robotics & Sensors, 2018), 'Swarm Intelligence - Volume 1: Principles, current algorithms and methods', Chap. 5, pp. 121-142, DOI: 10.1049/PBCE119F_ch5 [citation][year=2018]M. Mavrovouniotis, M. Van, S. Yang. Pheromone modification strategy for the dynamic travelling salesman problem with weight changes. IEEE Symposium Series on Computational Intelligence (SSCI 2017) Conference Proceedings, pp. 1-8, 2017 [citation][year=2018]Souza, Matheus. (2018) Algoritmo híbrido para o problema do caixeiro viajante dinâmico: otimização por colônia de formigas+ buscas locais. Dissertação de Mestrado do Programa de Pós-graduação em Informática, área de computação aplicada, da Universidade Federal de Santa Maria. Orientado por Felipe Martins Muller. [citation][year=2017]M. Mavrovouniotis, A. Ioannou, S. Yang. Pre-scheduled colony size variation in dynamic environments. Applications of Evolutionary Computation, LNCS, vol. 10200, pp. 128-139, Springer, 2017. [citation][year=2017]M. Mavrovouniotis, F. M. Müller, S. Yang. An ant colony optimization with local search for dynamic traveling salesman problems. IEEE Transactions on Cybernetics, vol. 47, no. 7, pp. 1743-1756, 2017. [citation][year=2017]M. Mavrovouniotis, C. Li, S. Yang. A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, vol. 33, pp. 1-17, Elsevier. 2017. [citation][year=2016]M. Mavrovouniotis, S. Yang. Empirical study on the effect of population size on MAX-MIN Ant System in dynamic environments. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC'16), pp. 853-860, 2016. [citation][year=2014]M. Mavrovouniotis and S. Yang. Ant Colony Optimization with Self-Adaptive Evaporation Rate in Dynamic Environments. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 47-54, IEEE, 2014 [citation][year=2014]M. Mavrovouniotis, S. Yang and Xin Yao. Multi-Colony Ant Algorithms for the Dynamic Travelling Salesman Problem. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 9-16, IEEE, 2014 [citation][year=2014]M. Mavrovouniotis and S. Yang. Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), pp. 1542-1549, IEEE Press, 2014. [citation][year=2014]M. Mavrovouniotis and S. Yang. Elitism-based immigrants for ant colony optimization in dynamic environments: adapting the replacement rate. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), pp. 1752-1759, IEEE Press, 2014. [publication]Tiago Baptista and Costa, E. , "Open-Ended Evolution of a Circadian Rhythm", in ECAL 2013, 12th European Conference on Artificial Life, 2013 [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "The Importance of the Learning Conditions in Hyper-Heuristics", in Genetic and Evolutionary Computation Conference (GECCO 2013), 2013 [citation][year=2018]Nyathi, T., & Pillay, N. (2018). Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms. Expert Systems with Applications, 104, 213-234. [citation][year=2018]Pillay, N., & Qu, R. Hyper-Heuristics: Theory and Applications. [citation][year=2018]Sevaux, M., Sörensen, K., & Pillay, N. (2018). Adaptive and multilevel metaheuristics. Handbook of Heuristics, 1-19. [citation][year=2017]D?az, X. F. C. S. (2017). School of Engineering and Sciences (Doctoral dissertation, Instituto Tecnológico y de Estudios Superiores de Monterrey). [citation][year=2017]Ryser-Welch, P. (2017). Evolving comprehensible and scalable solvers using CGP for solving some real-world inspired problems (Doctoral dissertation, University of York). [citation][year=2017]Mariani, T., Guizzo, G., Vergilio, S. R., & Pozo, A. T. Automatic Design of Algorithms Applied to the Multi-Objective TSP Problem. [citation][year=2016]Ortiz-Bayliss, José Carlos, Hugo Terashima-Marín, and Santiago Enrique Conant-Pablos. "A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems." Cognitive Computation 8.3 (2016): 429-441. [citation][year=2016]Martin, Matthew Allen. "Hyper-heuristics for the automated design of black-box search algorithms.". [citation][year=2016]Mariani, T., Guizzo, G., Vergilio, S. R., & Pozo, A. T. "Automatic Design of Algorithms Applied to the Multi-Objective TSP Problem". [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "Grammatical Evolution for the Multi-Objective Integration and Test Order Problem." Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. ACM, 2016. [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "A grammatical evolution hyper-heuristic for the integration and test order problem." GECCO. ACM, 2016. [citation][year=2015]Martin, Matthew A., and Daniel R. Tauritz. "Hyper-Heuristics: A Study On Increasing Primitive-Space." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015. [citation][year=2015]Martin, M. A. (2015). Hyper-heuristics for the automated design of black-box search algorithms. [citation][year=2015]Matthew A. Martin and Daniel R. Tauritz. 2014. A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic. In Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion (GECCO Comp '14) [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "Learning Selection Strategies for Evolutionary Algorithms", in Artificial Evolution (EA-2013), 2013 [citation][year=2018]Richter, S. N., & Tauritz, D. R. (2018, July). The automated design of probabilistic selection methods for evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1545-1552). ACM. [publication]Simões, A. and Costa, E. , "Extended Virtual Loser Genetic Algorithm for the Dynamic Traveling Salesman Problem", in Proceedings of the 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), Christian Blum (Ed.), pp. 869-876, Amsterdam, The Netherlands, 06-10, ACM, New York, NY, USA, 2013., 2013 [publication]Costa, E. and Rui L. Lopes , "Genetic Programming with Gene Regulatory Networks", in Proceedings of the Fifteenth annual conference on Genetic and evolutionary computation conference, 2013 [publication]Rui L. Lopes and Costa, E. , "Gearnet: Grammatical evolution with artificial regulatory networks", in Proceedings of the 15th annual conference on Genetic and Evolutionary Computation, 2013 [publication]Costa, E. and Rui L. Lopes , "Evolving an Harmonic Number Generator with ReNCoDe", in Proceedings of the Portuguese Conference on Artificial Intelligence, 2013 2012(3 publications) [publication]Simões, A. and Costa, E. , "Virtual Loser Genetic Algorithm for Dynamic Environments", in In C. Di Chio et al. (Eds.): EvoApplications 2012, LNCS 7248, pp. 539–548, 2012. Springer-Verlag Berlin Heidelberg 2012, Malaga, Spain, 10-14 April 2012., 2012 [publication]Lourenço, Nuno and Pereira, F.B. and Costa, E. , "Evolving Evolutionary Algorithms", in Genetic and Evolutionary Computation Conference (GECCO-2012), 2012 [citation][year=2018]Sevaux, M., Sörensen, K., & Pillay, N. (2018). Adaptive and multilevel metaheuristics. Handbook of Heuristics, 1-19. [citation][year=2018]Nyathi, T., & Pillay, N. (2018). Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms. Expert Systems with Applications, 104, 213-234. Chicago [citation][year=2017]Lima, Ricardo Henrique Remes de. "Um estudo sobre configuração automática do algoritmo de otimização por enxame de partículas multiobjetivo." (2017). [citation][year=2017]Fontoura, Vidal Daniel da. "Meta-heurísticas e hiper-heurísticas aplicadas ao problema de dobramento de proteínas." (2017). [citation][year=2017]Ryser-Welch, P. (2017). Evolving comprehensible and scalable solvers using CGP for solving some real-world inspired problems (Doctoral dissertation, University of York). [citation][year=2017]Mariani,T.,Guizzo,G.,Vergilio,S.R.,andPozo,A.T.AutomaticDesignofAlgorithms Applied to the Multi-Objective TSP Problem. [citation][year=2016]Mariani, T., Guizzo, G., Vergilio, S. R., and Pozo, A. T. (2016). A grammatical evolution hyper-heuristic for the integration and test order problem. In Genetic and Evolutionary Computation Conference [citation][year=2016]van Rijn, Sander, Hao Wang, Matthijs van Leeuwen, and Thomas Bäck. "Evolving the Structure of Evolution Strategies." arXiv preprint arXiv:1610.05231 (2016) [citation][year=2016]Franzin, Alberto, and Thomas Stützle. "Exploration of Metaheuristics through Automatic Algorithm Configuration Techniques and Algorithmic Frameworks." Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM, 2016. [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "A grammatical evolution hyper-heuristic for the integration and test order problem." GECCO. ACM, 2016. [citation][year=2016]Mariani, Thainá, Giovani Guizzo, Silvia R. Vergilio, and Aurora TR Pozo. "Grammatical Evolution for the Multi-Objective Integration and Test Order Problem." Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. ACM, 2016. [citation][year=2015]Ryser-Welch, Patricia, Julian F. Miller, and Shahriar Asta. "Generating human-readable algorithms for the Travelling Salesman Problem using Hyper-Heuristics." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015. [citation][year=2015]Ryser-Welch, Patricia, Julian F. Miller, and Shariar Asta. "Evolutionary Cross-Domain Hyper-Heuristics." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015. [citation][year=2015]Martin, Matthew Allen. Hyper-heuristics for the automated design of black-box search algorithms. Missouri University of Science and Technology, 2015. [citation][year=2015]Harris, S., Bueter, T., and Tauritz, D. R. (2015, July). A Comparison of Genetic Pro- gramming Variants for Hyper-Heuristics. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 1043- 1050). ACM. [citation][year=2015]Aboshosha, Ashraf, Kamal A. ElDahshan, Eman K. Elsayed, and Ahmed A. Elngar. "EA Based Dynamic Key Generation in RC4 Ciphering Applied to CMS." [citation][year=2015]Martin, Matthew A., and Daniel R. Tauritz. "Hyper-Heuristics: A Study On Increasing Primitive-Space." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015. [citation][year=2014]Ryser-Welch, Patricia, and Julian F. Miller. "A Review of Hyper-Heuristic Frameworks.", Proceedings of the 50th Anniversary Convention of the AISB [citation][year=2014]Martin, Matthew A., and Daniel R. Tauritz, "A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic", Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion. ACM, 2014 [citation][year=2013]Martin, Matthew A., and Daniel R. Tauritz. "Evolving black-box search algorithms employing genetic programming." Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion. ACM, 2013. [citation][year=2013]de Sá, Alex Guimarães Cardoso, and Gisele Lobo Pappa. "Towards a method for automatically evolving bayesian network classifiers." Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion. ACM, 2013. [publication]Simões, A. and Costa, E. , "Enhancing the Virtual Loser Genetic Algorithm for Dynamic Environments", in Genetic and Evolutionary Computation Conference (GECCO 2012), 2012 2011(8 publications) [publication]Simões, A. and Costa, E. and Carvalho, R. and João R. Campos , "A Study on Population's Diversity for Dynamic Environments", in 2011 International Conference on Adaptive and Natural Computing Algorithms, ICANNGA'11, 2011 [citation][year=2012]Carlos Azevedo, Renato Belo, and Aluizio FR Araújo. "Non-Dominance Landscapes for Guiding Diversity Generation in Dynamic Environments.", Universidade Federal de Pernambuco, 2012. [publication]Simões, A. and Costa, E. , "CHC-based Algorithms for the Dynamic Traveling Salesman Problem", in EvoWorkshops 2011, Applications of Evolutionary Computing, 2011 [citation][year=2015]Groba, C., Sartal, A., & Vázquez, X. H. (2015). Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: An application to fish aggregating devices. Computers & Operations Research, 56, 22-32. [citation][year=2014]Groba, C., Sartal, A., & Vázquez, X. H. (2014). Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: An application to fish aggregating devices. Computers & Operations Research. [citation][year=2014]Mavrovouniotis, M., & Yang, S. (2014, July). Elitism-based immigrants for ant colony optimization in dynamic environments: Adapting the replacement rate. In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 1752-1759). IEEE. [citation][year=2014]Tinós, R., Whitley, D., & Howe, A. (2014, July). Use of explicit memory in the dynamic traveling salesman problem. In Proceedings of the 2014 conference on Genetic and evolutionary computation (pp. 999-1006). ACM. [citation][year=2014]Zhang, Z., Yue, S., Liao, M., & Long, F. (2014). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, 18(1), 185-206. [citation][year=2013]Zhuhong Zhang, Lei Wang, and Min Liao (2013). Adaptive sampling immune algorithm solving joint chance-constrained programming. Journal of Control Theory and Applications 11, no. 2, pp. 237-246, Springer, 2013. [citation][year=2013]Zhuhong Zhang, Shigang Yue, Min Liao, and Fei Long (2013). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, pp. 1-22, Springer 2013. [citation][year=2012]Patryk Filipiak and Piotr Lipi?ski (2012). “Parallel CHC Algorithm for Solving Dynamic Traveling Salesman Problem Using Many-Core GPU”. Artificial Intelligence: Methodology, Systems, And Applications, Lecture Notes in Computer Science, 2012, Volume 7557/2012, pp. 305-314, Springer, 2012. [publication]Rui L. Lopes and Costa, E. , "ReNCoDe: a regulatory network computational device", in 14th European Conference on Genetic Programming (EuroGP 2011), 2011 [publication]Simões, A. and Costa, E. , "Memory-based CHC Algorithms for the Dynamic Traveling Salesman Problem", in 2011 Genetic and Evolutionary Computation Conference (GECCO 2011), 2011 [citation][year=2015]Segura, C., Coello, C. A. C., Miranda, G., & León, C. (2015). Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization. Annals of Operations Research, 1-34. [citation][year=2015]Bravo, Y., Luque, G., & Alba, E. (2015). Global memory schemes for dynamic optimization. Natural Computing, 1-15. [citation][year=2013]Carlos Segura, Carlos A. Coello, Gara Miranda, Coromoto León (2013). Using multi-objective evolutionary algorithms for single-objective optimization. 4OR 11.3 (2013): 201-228. [citation][year=2013]Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, Xin Yao (2013). Evolutionary Dynamic Optimization: Methodologies. Evolutionary Computation for Dynamic Optimization Problems, Studies in Computational Intelligence Volume 490, pp 39-64, Springer 2013 [citation][year=2013]Carlos Segura, Carlos A. Coello Coello, Eduardo Segredo, Gara Miranda, and Coromoto Leon (2013). Improving the diversity preservation of multi-objective approaches used for single-objective optimization. In 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 3198-3205. IEEE, 2013. [citation][year=2012]T. T. Nguyen, S. Yang, and J. Branke (2012). “Evolutionary dynamic optimization: A survey of the state of the art”. Swarm and Evolutionary Computation, Elsevier, 2012. [publication]Rui L. Lopes and Costa, E. , "Using Feedback Connections in ReNCoDe", in 2011 Genetic and Evolutionary Computation Conference (GECCO 2011), 2011 [publication]Rui L. Lopes and Costa, E. , "The Squares Problem and a Neutrality Analysis with ReNCoDe", in 15th Portuguese Conference on Artificial Intelligence, 2011 [publication]Tiago Baptista and Costa, E. , "The Evolution of Foraging in an Open-Ended Simulation Environment", in Progress in Artificial Intelligence: 15th Portuguese Conference on Artificial Intelligence, 2011 [publication]Leonor Melo and Pereira, F.B. and Costa, E. , "Multi-caste Ant Colony Optimization Algorithms", in 15th Portuguese Conference on Artificial Intelligence, 2011 2010(1 publication) [publication]Sousa, J. and Costa, E. , "EpiAL - an epigenetic approach for an Artificial Life Model", in International Conference on Agents and Artificial Intelligence, 2010 2009(6 publications) [publication]Tomé, P. and Costa, E. and Amaral, L. , "The re-use of experience trough the use o CBR in information systems modelling", in International Conference on Agents and Artificial Intelligence, 2009 [publication]Menezes, T. and Costa, E. , "Coevolution of competing agents species in a game-like environment", in EvoGAMES 2009, 2009 [publication]Simões, A. and Costa, E. , "The Influence of Population and Memory Sizes on the Evolutionary Algorithm's Performance for Dynamic Environments", in 6th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EVOSTOC 2009), vol. 5484, 2009 [citation][year=2015]du Plessis, M. C., Engelbrecht, A. P., & Calitz, A. (2015, January). Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments. In Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII (pp. 114-128). ACM. [citation][year=2013]Richter, H. (2013). Dynamic Fitness Landscape Analysis. In Evolutionary Computation for Dynamic Optimization Problems (pp. 269-297). Springer Berlin Heidelberg. [citation][year=2012]Mathys Cornelius du Plessis (2012). “Adaptive Multi-Population Diferential Evolution for Dynamic Environments”. PhD Thesis, University of Pretoria, 2012. [publication]Simões, A. and Costa, E. , "Improving Prediction in Evolutionary Algorithms for Dynamic Environments", in GECCO 2009, 2009 [citation][year=2015]Chen, X., Zhang, D., & Zeng, X. (2015, November). A Stable Matching-Based Selection and Memory Enhanced MOEA/D for Evolutionary Dynamic Multiobjective Optimization. In Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on (pp. 478-485). IEEE. [citation][year=2015]Yang, S. (2015, July). Evolutionary computation for dynamic optimization problems. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (pp. 629-649). ACM. [citation][year=2013]Uluda?, G., Kiraz, B., Etaner-Uyar, A. ?., & Özcan, E. (2013). A hybrid multi-population framework for dynamic environments combining online and offline learning. Soft Computing, 17(12), 2327-2348. [citation][year=2013]Kisiel-Dorohinicki, M. (2013). Evolutionary multi-agent systems in non-stationary environments. Computer Science, 14. [citation][year=2013]Byrski, A., & Schaefer, R. (2013). Markov Chain Analysis of Agent-Based Evolutionary Computing in Dynamic Optimization. Procedia Computer Science, 18, 1475-1484. [citation][year=2013]Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2013). Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 2013. [citation][year=2013]Hajer Ben-Romdhane, Enrique Alba, and Saoussen Krichen (2013). Best practices in measuring algorithm performance for dynamic optimization problems. Soft Computing, pp. 1-13, Springer, 2013. [citation][year=2013]Aleksander Byrski, and Robert Schaefer (2013). Markov Chain Analysis of Agent-Based Evolutionary Computing in Dynamic Optimization. Procedia Computer Science 18, pp. 1475-1484, Elsevier, 2013. [citation][year=2013]Enrique Alba, H. Ben-Romdhane, S. Krichen, B. Sarasola (2013). BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems" Evolutionary Computation for Dynamic Optimization Problems, pp. 171-191, Springer Berlin Heidelberg, 2013. [citation][year=2013]Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, Xin Yao (2013). Evolutionary Dynamic Optimization: Methodologies Evolutionary Computation for Dynamic Optimization Problems, pp. 39-64, Springer Berlin Heidelberg, 2013. [citation][year=2013]Hongfeng Wang, Shengxiang Yang (2013). Memetic Algorithms for Dynamic Optimization Problems." Evolutionary Computation for Dynamic Optimization Problems, pp. 137-170, Springer Berlin Heidelberg, 2013. [citation][year=2013]Shengxiang Yang (2013). Evolutionary computation for dynamic optimization problems. Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, pp. 667-682, ACM, 2013. [citation][year=2013]Marek Kisiel-Dorohinicki (2013). Evolutionary Multi-Agent Systems In Non-Stationary Environments." Computer Science 14.4, 2013. [citation][year=2012]T. T. Nguyen, S. Yang, and J. Branke (2012). “Evolutionary dynamic optimization: A survey of the state of the art”. Swarm and Evolutionary Computation, Elsevier, 2012. [citation][year=2012]Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2012). "Metaheuristics for dynamic combinatorial optimization problems." IMA Journal of Management Mathematics, 2012. [citation][year=2011]Chen Li (2011). Dynamic Optimization Algorithms. Journal of Wuhan University: Natural Science, 2011. [publication]Simões, A. and Costa, E. , "Prediction in Evolutionary Algorithms for Dynamic Environments Using Markov Chains and Nonlinear Regression", in GECCO 2009, 2009 [citation][year=2013]Haobo Fu, Bernhard Sendhoff, Ke Tang, and Xin Yao (2013). Finding robust solutions to dynamic optimization problems. In Applications of Evolutionary Computation, pp. 616-625. Springer Berlin Heidelberg, 2013. [citation][year=2013]Carlos RB Azevedo, Fernando J. Von Zuben (2013). Anticipatory Stochastic Multi-Objective Optimization for uncertainty handling in portfolio selection. IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 157-164, IEEE, 2013. [citation][year=2013]Uluda?, G., Kiraz, B., Etaner-Uyar, A. ?., & Özcan, E. (2013). A hybrid multi-population framework for dynamic environments combining online and offline learning. Soft Computing, 17(12), 2327-2348. [citation][year=2012]Mathys Cornelius du Plessis (2012). “Adaptive Multi-Population Diferential Evolution for Dynamic Environments”. PhD Thesis, University of Pretoria, 2012. [citation][year=2011]Chen Li (2011). Dynamic Optimization Algorithms. Journal of Wuhan University: Natural Science, 2011. [citation][year=2010]Di Chio, Cecilia, et al., eds. (2010). Applications of Evolutionary Computation: EvoApplications 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Istanbul, Turkey, April 7-9, 2010, Proceedings. Vol. 1. Springer, 2010. [publication]Leonor Melo and Pereira, F.B. and Costa, E. , "MC-ANT: a Multi-colony Ant Algorithm", in Artificial Evolution (EA '09), 2009 [citation][year=2019]Bouzbita S., El Afia A., Faizi R. (2019) Hidden Markov Model Classifier for the Adaptive ACS-TSP Pheromone Parameters. In: Talbi EG., Nakib A. (eds) Bioinspired Heuristics for Optimization. Studies in Computational Intelligence, vol 774. Springer, Cham [citation][year=2019]Abdelbar, A. M., Salama, K. M. (2019) Parameter Self-Adaptation in an Ant Colony Algorithm for Continuous Optimization. In: IEEE Access, vol. 7, pp. 18464-18479. doi: 10.1109/ACCESS.2019.2896104 [citation][year=2018]Bouzbita, S., El Afia, A., Faizi, R. Parameter Adaptation for Ant Colony System Algorithm using Hidden Markov Model for TSP Problems, Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, {LOPAL}, ACM, 2018, doi:10.1145/3230905.3230962 [citation][year=2017]Bouzbita, S., El Afia, A., Faizi, R., "A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter", In: Proceedings of 2016 5th International Conference on Multimedia Computing and Systems, ICMCS 2016, pp. 633-638, April 2017, ISBN: 9781509051465, DOI: https://doi.org/10.1109/ICMCS.2016.7905544 [citation][year=2016]Kengo Katayama , Yusuke Okamoto, Elis Kulla, Noritaka Nishihara, "Variable Neighborhood Search Algorithms for the Node Placement Problem in Multihop Networks", Advances on Broad-Band Wireless Computing, Communication and Applications, LNCS, 2, pp. 631-638, October 2016 [citation][year=2015]Rafid Sagban, Ku Ruhana KuMahamud and Muhamad Shahbani Abu Bakar, Nature-inspired Parameter Controllers for ACO-based Reactive Search, Research Journal of Applied Sciences, Engineering and Technology 10(1): 109117, 2015 [citation][year=2014]K. Katayama, Y. Akagi, E. Kulla, H. Minamihara, and N. Nishihara, “New Kick Operators in Iterated Local Search Based Metaheuristic for Solving the Node Placement Problem in Multihop Networks,” in 2014 17th International Conference on Network-Based Information Systems, 2014, pp. 141–148. [citation][year=2013]Ana Maria A.C. Rocha, M. Fernanda P. Costa, Edite M.G.P. Fernandes, Distribution based artificial fish swarm in continuous global optimization, Atas do XVI Congresso da Associação Portuguesa de Investigação Operacional, Oliveira, José F.; Vaz, Clara B. (Eds.), Instituto Politécnico de Bragança, p. 306-312, 2013. [citation][year=2013]LIU Rui-jie, WANG Li-juan, SHI Yuan. Multi-Colony Ant Algorithm Applied to the Rectangular Pieces Layout Optimization. Journal of Jiangnan University(Natural Science Edition). 2013, 12(3) [citation][year=2012]P Deepalakshmi, S Radhakrishnan. Online parameter tuning using Particle Swarm Optimization for ant-based QoS routing in mobile ad-hoc networks. International Journal of Hybrid Intelligent Systems, IOS Press, 2012. [citation][year=2011]Stützle, T., López-Ibánez, M., Pellegrini, P., Maur, M., De Oca, M. M., Birattari, M., & Dorigo, M. (2011). Parameter adaptation in ant colony optimization. In Autonomous search (pp. 191-215). Springer Berlin Heidelberg. [citation][year=2010]Gómez Díaz, Yudel Rodrigo, Algoritmos que combinan conjuntos aproximados y optimización basada en colonias de hormigas para la selección de rasgos. Extensión a múltiples fuentes de datos, PhD Thesis, Universidad Central “Marta Abreu” de Las Villas. Facultad de Matemática, Física y Computación. Departamento Ciencias de la Computación, 2010 [citation][year=2010]Thomas Stutzle, Manuel Lopez-Ibanez, Paola Pellegrini, Michael Maur, Marco Montes de Oca, Mauro Birattari, and Marco Dorigo, Parameter Adaptation in Ant Colony Optimization, IRIDIA " Technical Report Series, Technical Report No. TR/IRIDIA/2010-002, January 2010 2008(5 publications) [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Golomb Rulers: a Fitness Landscape Analysis", in IEEE Congress on Evolutionary Computation, 2008 [publication]Simões, A. and Costa, E. , "Evolutionary Algorithms for Dynamic Environments: Prediction using Linear Regression and Markov Chains", in PPSN X, 2008 [citation][year=2015]Li, C., Nguyen, T. T., Yang, M., Yang, S., & Zeng, S. (2015). Multi-population methods in unconstrained continuous dynamic environments: The challenges. Information Sciences, 296, 95-118. [citation][year=2015]Filipiak, P., & Lipinski, P. (2015). Making IDEA-ARIMA efficient in dynamic constrained optimization problems. In Applications of Evolutionary Computation (pp. 882-893). Springer International Publishing. [citation][year=2015]Bravo, Y., Luque, G., & Alba, E. (2015). Global memory schemes for dynamic optimization. Natural Computing, 1-15. [citation][year=2015]Chen, X., Zhang, D., & Zeng, X. (2015, November). A Stable Matching-Based Selection and Memory Enhanced MOEA/D for Evolutionary Dynamic Multiobjective Optimization. In Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on (pp. 478-485). IEEE. [citation][year=2014]Li, C., Yang, S., & Yang, M. (2014). An adaptive multi-swarm optimizer for dynamic optimization problems. [citation][year=2014]Li, C., Nguyen, T. T., Yang, M., Yang, S., & Zeng, S. (2014). Multi-population methods in unconstrained continuous dynamic environments: the challenges.Information Sciences. [citation][year=2014]Richter, H. (2014). Fitness Landscapes That Depend on Time. In Recent Advances in the Theory and Application of Fitness Landscapes (pp. 265-299). Springer Berlin Heidelberg. [citation][year=2014]Filipiak, P., & Lipinski, P. (2014). Infeasibility Driven Evolutionary Algorithm with Feed-Forward Prediction Strategy for Dynamic Constrained Optimization Problems. In Applications of Evolutionary Computation (pp. 817-828). Springer Berlin Heidelberg. [citation][year=2014]Mukherjee, R., Patra, G. R., Kundu, R., & Das, S. (2014). Cluster-based differential evolution with Crowding Archive for niching in dynamic environments. Information Sciences, 267, 58-82. [citation][year=2013]Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2013). Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 2013. [citation][year=2013]Hajer Ben-Romdhane, Enrique Alba, and Saoussen Krichen (2013). Best practices in measuring algorithm performance for dynamic optimization problems. Soft Computing, pp. 1-13, Springer, 2013. [citation][year=2013]Hendrik Richter and Shengxiang Yang (2013). Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, pp. 1–28, Springer, 2013. [citation][year=2013]Danial Yazdani, Babak Nasiri, Alireza Sepas-Moghaddam, and Mohammad Reza Meybodi (2013). A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization. Applied Soft Computing, Elsevier, 2013. [citation][year=2013]Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, Xin Yao (2013). Evolutionary Dynamic Optimization: Methodologies. Evolutionary Computation for Dynamic Optimization Problems, Studies in Computational Intelligence Volume 490, pp 39-64, Springer 2013 [citation][year=2013]S Yang, TT Nguyen, C Li (2013). Evolutionary Dynamic Optimization: Test and Evaluation Environments. Studies in Computational Intelligence, Volume 490, pp. 3-37, Springer 2013. [citation][year=2013]Enrique Alba, H. Ben-Romdhane, S. Krichen, B. Sarasola (2013). BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems" Evolutionary Computation for Dynamic Optimization Problems, pp. 171-191, Springer Berlin Heidelberg, 2013. [citation][year=2013]Wang, H., & Yang, S. (2013). Memetic Algorithms for Dynamic Optimization Problems. In Evolutionary Computation for Dynamic Optimization Problems (pp. 137-170). Springer Berlin Heidelberg. [citation][year=2013]Uluda?, G., Kiraz, B., Etaner-Uyar, A. ?., & Özcan, E. (2013). A hybrid multi-population framework for dynamic environments combining online and offline learning. Soft Computing, 17(12), 2327-2348. [citation][year=2012]Hendrik Richter and Shengxiang Yang (2012). Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, pp. 1–28, Springer 2012. [citation][year=2012]C. Li, S. Yang, M. Yang (2012). Maintaining diversity by clustering in dynamic environments. 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, IEEE 2012. [citation][year=2012]T. T. Nguyen, S. Yang, and J. Branke (2012). “Evolutionary dynamic optimization: A survey of the state of the art”. Swarm and Evolutionary Computation, Elsevier, 2012. [citation][year=2012]Changhe Li, Shengxiang Yang. A general framework of multipopulation methods with clustering in undetectable dynamic environments. IEEE Transactions on Evolutionary Computation, 16(4), pp. 556-577, IEEE, 2012. [citation][year=2012]Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2012). "Metaheuristics for dynamic combinatorial optimization problems." IMA Journal of Management Mathematics, 2012. [citation][year=2011]Hendrik Richter, Franz Dietel (2011), “Solving Dynamic Constrained Optimization Problems with Asynchronous Change Pattern”. In C. Di Chio et al. (Eds.): EvoApplications 2011, Part I, LNCS 6624, pp. 334-343, Springer-Verlag Berlin Heidelberg 2011, Torino, Italy, 27-29 April 2011. [citation][year=2011]J. Lepagnot, A. Nakib, H. Oulhadj, P. Siarry (2011). “Brain cine MRI segmentation based on a multiagent algorithm for dynamic continuous optimization”. 2011 IEEE Congress on Evolutionary Computation, pp. 1695-1702, IEEE, 2011. [citation][year=2011]C. Li and S. Yang (2011). A general framework of multi-population methods with clustering in undetectable dynamic environments. IEEE Transactions on Evolutionary Computation, September 2011. IEEE Press. [citation][year=2011]Li Chen, Lixin Ding, Xin Du (2011). Genetic algorithm with Particle Filter for dynamic optimization problems. 3rd International Conference on Computer Research and Development (ICCRD), 2011, pp. 452- 457, IEEE 2011. [citation][year=2011]Chen Li (2011). Dynamic Optimization Algorithms. Journal of Wuhan University: Natural Science, 2011. [citation][year=2010]H. Richter (2010). "Evolutionary Optimization and Dynamic Fitness Landscapes?. Evolutionary Algorithms and Chaotic Systems, Studies in Computational Intelligence, Vol. 267/2010, pp. 409-446, Springer, 2010. [citation][year=2010]H. Richter (2010). Memory Design for Constrained Dynamic Optimization Problems. C. Di Chio et al. (Eds.): EvoApplications 2010, Part I, LNCS 6024, pp. 552"561, Springer-Verlag Berlin Heidelberg, 2010. [publication]Tomé, P. and Costa, E. and Amaral, L. , "Improving case retrieval performance through the use o clustering techniques", in International Conference on Enterprise Information Systems, 2008 [publication]Menezes, T. and Costa, E. , "Artificial Brains as Networks of Computational Building Blocks", in 5th European Conference on Complex Systems, 2008 [publication]Goncalves, A. and Costa, E. , "A computational model of gene regulatory network and its topological properties", in 11th International Conference on the Simulation and Synthesis of Living Systems (ALIFE XI), 2008 [citation][year=2011]P. Tonelli and J-B Mouret and S. Doncieux, Influence of promoter lenght on network convergence in GRN-based evolutionary algorithms, in Advances of Artificial Life, LNCS, Vol. 5778, 2011. [citation][year=2011]Y. Jin and Y. Meng, Morphogenetic robotics: an emerging new field in developmental robotics, in IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, Vol. 41, #2, March 2011. [citation][year=2010]T. Hu and W. Banzhaf, Evolvability and speed of evolutionary algorithms in light of recent developments in biology, in Journal of Artificial Evolution and Applications, Vol. 2010, January 2010. 2007(10 publications) [publication]Menezes, T. and Costa, E. , "The Gridbrain: an Heterogeneous Network for Open Evolution in 3D Environments", in The First IEEE Symposium on Artificial Life, 2007 [publication]Simões, A. and Costa, E. , "Variable-size Memory Evolutionary Algorithm to Deal with Dynamic Environments", in EvoWorkshops 2007, 2007 [citation][year=2015]Zhu, T., Luo, W., & Yue, L. (2015). Dynamic optimization facilitated by the memory tree. Soft Computing, 19(3), 547-566. [citation][year=2014]Richter, H. (2014). Fitness Landscapes That Depend on Time. In Recent Advances in the Theory and Application of Fitness Landscapes (pp. 265-299). Springer Berlin Heidelberg. [citation][year=2014]Zhu, T., Luo, W., & Yue, L. (2014). Dynamic optimization facilitated by the memory tree. Soft Computing, 1-20. [citation][year=2014]Zhang, Z., Yue, S., Liao, M., & Long, F. (2014). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, 18(1), 185-206. [citation][year=2013]Zhuhong Zhang, Shigang Yue, Min Liao, and Fei Long (2013). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, pp. 1-22, Springer, 2013. [citation][year=2013]Hendrik Richter and Shengxiang Yang (2012). Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, pp. 1–28, Springer 2013. [citation][year=2012]Hendrik Richter and Shengxiang Yang (2012). Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, pp. 1–28, Springer 2012 [citation][year=2012]Hendrik Richter (2012). Artificial Immune Systems, Dynamic Fitness Landscapes, and the Change Detection Problem. Bio-Inspired Computational Algorithms and Their Applications, pp. 336-350, Dr. Shangce Gao (Ed.), ISBN: 978-953-51-0214-4, InTech, 2012. [citation][year=2011]Hendrik Richter, Franz Dietel (2011), “Solving Dynamic Constrained Optimization Problems with Asynchronous Change Pattern”. In C. Di Chio et al. (Eds.): EvoApplications 2011, Part I, LNCS 6624, pp. 334-343, Springer-Verlag Berlin Heidelberg 2011, Torino, Italy, 27-29 April 2011. [citation][year=2011]H. Meneses Ponce, M. Inostroza-Ponta (2011). Evaluating memory schemas in a Memetic Algorithm for the Quadratic Assignment Problem. XXX International Conference of the Chilean Computer Science Society (SCCC), Chile 2011. [citation][year=2011]H. Meneses Ponce, M. Inostroza-Ponta (2011). Esquemas de Memoria en Metaheuristicas: Mejora de un Algoritmo Memetico para el Problema de Asignacion Cuadratica. Jornadas Chilenas de Computacion, Chile 2011. [citation][year=2011]Tao Zhu, Wenjian Luo, Zhifang Li (2011). An adaptive strategy for updating the memory in Evolutionary Algorithms for dynamic optimization. 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp.8-15, IEEE 2011. [citation][year=2010]H. Richter (2010). "Evolutionary Optimization and Dynamic Fitness Landscapes. Evolutionary Algorithms and Chaotic Systems, Studies in Computational Intelligence, Vol. 267/2010, pp. 409-446, Springer, 2010. [citation][year=2010]H. Richter (2010). Memory Design for Constrained Dynamic Optimization Problems. C. Di Chio et al. (Eds.): EvoApplications 2010, Part I, LNCS 6024, pp. 552"561, Springer-Verlag Berlin Heidelberg, 2010. [citation][year=2010]Hao Chen, Ming Li, Xi Chen (2010), A Predator-Prey Cellular Genetic Algorithm for Dynamic Optimization Problems. 2nd International Conference on Information Engineering and Computer Science (ICIECS), pp. 1-6, IEEE 2010. [citation][year=2010]Hao Chen, Ming Li, Xi Chen (2010), Hybrid Memory Scheme for Genetic Algorithm in Dynamic Environments. Journal of Applied Sciences - Electronics and Information Engineering, Vol 28, nº 5, pp. 540-545, 2010. [citation][year=2010]Hao Chen, Ming Li, Xi Chen (2010), Cellular Genetic Algorithm with Density Dependence for Dynamic Optimization Problem. Journal of Information and Computing Science, Vol. 5, No. 4, pp. 287-298, World Academic Press 2010. [citation][year=2009]J. Tim Hendtlass, Irene Moser, Marcus Randal (2009). Dynamic Problems and Nature Inspired Meta-heuristics. Biologically-Inspired Optimisation Methods , Series Studies in Computational Intelligence, Volume 210, pp. 79-109, Springer 2009. [citation][year=2009]H. Richter (2009). Detecting change in dynamic fitness landscapes, pp.1613-1620, 2009 IEEE Congress on Evolutionary Computation, IEEE Press, 2009. [citation][year=2009]H. Richter, S. Yang (2009). Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 13, Number 12, pp. 1163-1173, Springer 2009. [citation][year=2009]H. Richter (2009). Change detection in dynamic fitness landscapes: An immunological approach. In: World Congress on Nature and Biologically Inspired Computing (NaBIC'09), (Eds.: A. Abraham, A. Carvalho, F. Herrera, V. Pai), IEEE Research Publishing Services, Singapore, 719-724, IEEE Press, 2009. [publication]Menezes, T. and Costa, E. , "Designing for Surprise", in ECAL 2007, 2007 [citation][year=2009]uan Ramón Rabuñal Dopico (University of Coruña, Spain); Javier Pereira Loureiro (University of Coruña, Spain); Mónica Miguélez Rico (University of Coruña, Spain), Simulation of the Action Potential in the Neuron's Membrane in Artificial Neural Networks, in Advancing Artificial Intelligence through Biological Process Applications, Ana B. Porto Pazos (Coruna University, Spain); Alejandro Pazos Sierra (Coruna University, Spain); Washington Buño Buceta (Cajal Institute, Spanish Council for Scientific Research, Spain) (Eds.), Cap V, pp 74-93, 2009. [publication]Simões, A. and Costa, E. , "Variable-size Memory Evolutionary Algorithm: Studies on Replacing Strategies and Diversity in Dynamic Environments", in Genetic and Evolutionary Computation Conference (GECCO-2007), 2007 [citation][year=2009]Richter, H and Yang, S. X. Learning behaviour in abstract memory schemes for dynamic optimization problems. Soft Computing, 13 (12): 1163-1173 October 2009 [publication]Simões, A. and Costa, E. , "Improving Memory's Usage in Evolutionary Algorithms for Changing Environments", in IEEE 2007 Congress on Evolutionary Computation, 2007 [citation][year=2014]Zhu, T., Luo, W., & Yue, L. (2014, July). Combining multipopulation evolutionary algorithms with memory for dynamic optimization problems. In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 2047-2054). IEEE. [citation][year=2014]Morales-Enciso, S., & Branke, J. (2014). Response surfaces with discounted information for global optima tracking in dynamic environments. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) (pp. 57-69). Springer International Publishing. [citation][year=2012]T. T. Nguyen, S. Yang, and J. Branke (2012). “Evolutionary dynamic optimization: A survey of the state of the art”. Swarm and Evolutionary Computation, Elsevier, 2012. [citation][year=2012]Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2012). "Metaheuristics for dynamic combinatorial optimization problems." IMA Journal of Management Mathematics, 2012. [citation][year=2009]J. Tim Hendtlass, Irene Moser, Marcus Randal (2009). Dynamic Problems and Nature Inspired Meta-heuristics. Biologically-Inspired Optimisation Methods , Series Studies in Computational Intelligence, Volume 210, pp. 79-109, Springer 2009. [citation][year=2009]Xin Yu, Ke Tang, Tianshi Chen, Xin Yao (2009). Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Journal of Memetic Computing, Vol. 1, 1, pp. 3-24, Springer 2009. [publication]Leitão, T. and Pereira, F.B. and Tavares, J. and Costa, E. , "Niching Techniques: a Study on the Cluster Geometry Optimization Problem", in Genetic and Evolutionary Computation Conference (GECCO 2007), 2007 [publication]Tomé, P. and Costa, E. and Amaral, L. , "Improving data modelling through the use of case-based reasoning", in CAISE\'07, 2007 [publication]Tomé, P. and Costa, E. and Amaral, L. , "A tool-neutral framework for systematic experience reuse in architecture description", in 14th Enterprise Architecture Practioners, 2007 [publication]Tiago Baptista and Costa, E. , "The Emergence of a Circadian Rhythm in a Multi-Agent Simulation", in European Conference on Complex Systems 2007, 2007 [publication]Goncalves, A. and Costa, E. , "HeRoN: a computational model of gene regulatory networks", in 13th Portuguese Conference on Artificial Intelligence, 2007 2006(5 publications) [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "The Role of Representation on the Multidimensional Knapsack Problem by means of Fitness Landscape Analysis", in IEEE Congress on Evolutionary Computation, 2006 [citation][year=2015]An Analysis of the Fitness Landscape of Travelling Salesman Problem MH Tayarani-N, A Prügel-Bennett - Evolutionary computation, 2015 - MIT Press [citation][year=2015]A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem MF Tasgetiren, QK Pan, D Kizilay… - … (CEC), 2015 IEEE …, 2015 - ieeexplore.ieee.org [citation][year=2015]Análise da aprendizagem de ligações em otimização evolutiva JP Martins - teses.usp.br [citation][year=2011]Yanghui Wu; McCall, J.; Corne, D.; , "Fitness landscape analysis of Bayesian network structure learning," Evolutionary Computation (CEC), 2011 IEEE Congress on , vol., no., pp.981-988, 5-8 June 2011 doi: 10.1109/CEC.2011.5949724 [citation][year=2009]P. Rohlfshagen, X. Yao (2009). The Dynamic Knapsack Problem Revisited: A New Benchmark Problem for Dynamic Combinatorial Optimisation. Proceedings of the Evoworkshops 2009, Lecture Notes on Computer Science 53484, pp. 745-754, Spinger-Verlag. [citation][year=2009]E Özcan, C Ba?aran. A case study of memetic algorithms for constraint optimization. Soft Computing, 2009, Springer. [publication]Menezes, T. and Tiago Baptista and Costa, E. , "Towards the Generation of Complex Game Worlds", in IEEE Symposium on Computational Intelligence and Games, 2006 [citation][year=2012]Cardamone, L. (2012), ‘Evolutionary Learning and Search-Based Content Generation in Computer Games’, Plitecnico di Milano. [citation][year=2012]Johansson, D. (2012). 'Complex Systems in Video Games-a literature survey'. [publication]Menezes, T. and Costa, E. , "A First Order Language to Coevolve Agents in Complex Social Simulations", in European Conference on Complex Systems 2006, 2006 [publication]Menezes, T. and Tiago Baptista and Costa, E. , "Bitbang'a Library for Modern Game AI", in International Digital Games Conference (iDiG 2006), 2006 [publication]Tiago Baptista and Menezes, T. and Costa, E. , "BitBang: A Model and Framework for Complexity Research", in European Conference on Complex Systems 2006, 2006 [citation][year=2013]Caillou, P., Gil-Quijano, J., & Zhou, X. (2013). 'Automated observation of multi-agent based simulations: a statistical analysis approach'. Studia Informatica Universalis. [citation][year=2012]Caillou, P. and Gil-Quijano, J. (2012), ‘Description automatique de dynamiques de groupes dans des simulations à base d'agents’, JFSMA 2012. [citation][year=2011]Haidar, A., An Adaptive Document Classifier Inspired by T-Cell Cross Regulation in the Immune System, PhD Thesis (Indiana Univeristy, 2011). 2005(5 publications) [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Golomb Rulers: Experiments with Marks Representation", in 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), 2005 [publication]Tavares, J. and Leitão, T. and Pereira, F.B. and Costa, E. , "Evolving Segments Length in Golomb Rulers", in 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), 2005 [citation][year=2011]P Häcker, S Uhlich, B Yang. Fast beampattern evaluation by polynomial rooting. Advances in Radio Science, 2011. [publication]Sara Silva and Costa, E. , "Resource-Limited Genetic Programming: The Dynamic Approach", in Genetic and Evolutionary Computation Conference (GECCO-2005), 2005 [citation][year=2010]Whigham PA, Dick G (2010). Implicitly Controlling Bloat in Genetic Programming. IEEE Transactions on Evolutionary Computation, 14(2): 173--190. [citation][year=2010]Costa EO, Pozo A, Vergilio SR (2010). A Genetic Programming Approach for Software Reliability Modeling. IEEE Transactions on Reliability, 59(1): 222--230. [citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39. [citation][year=2009]Seo K, Hyun S, Goodman ED (2009). Tree-structure-aware GP operators for automatic gait generation of quadruped robot. GECCO-2009, pp. 2155-2160. [citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza). [citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1�6. [citation][year=2008]Kuyucu T, Trefzer M, Greensted A, Miller J, Tyrrell A (2008). Fitness functions for the unconstrained evolution of digital circuits. In 9th IEEE World Congress on Computational Intelligence (WCCI 2008), 2589-2596. [citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia. [citation][year=2008]Chu D, Rowe JE (2008). Crossover operators to control size growth in linear GP and variable length GAs. 2008 IEEE Congress on Evolutionary Computation, vols 1-8: 336-343. [citation][year=2007]Clifton M, Fang G (2007). Genetic Programming in Robot Exploration. In Proc 2007 IEEE International Conference on Mechatronics and Automation (ICMA 2007), 451"456. [citation][year=2006]Luke S, Panait L (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation 14(3): 309-344. [citation][year=2006]Costa EO, Pozo A (2006). A (mu + lambda) GP Algorithm and its use for Regression Problems. In Proc 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2006), 10-17. [citation][year=2006]Costa EO (2006). Proposta de um Algoritmo de Programacao Genetica Baseado em Estrategias Evolucionarias. MSc Thesis, Universidade Federal do Parana, Curitiba, Brasil. [citation][year=2006]Costa EO, Pozo A (2006). A new approach to genetic programming based on evolution strategies. In Proc 2006 IEEE International Conference on Systems, Man, and Cybernetics, 4832-4837. [publication]Sara Silva and Silva, P. and Silva, P. and Costa, E. , "Resource-Limited Genetic Programming: Replacing Tree Depth Limits", in 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), 2005 [citation][year=2009]Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK. [citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza). [citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1"6. [citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia. [citation][year=2008]Chu D, Rowe JE (2008). Crossover operators to control size growth in linear GP and variable length GAs. 2008 IEEE Congress on Evolutionary Computation, vols 1-8: 336-343. [citation][year=2006]Da Costa LE, Landry JA. Relaxed genetic programming. In Proc Genetic and Evolutionary Computation Conference (GECCO"2006), 937"938. [citation][year=2006]Costa EO (2006). Proposta de um Algoritmo de Programação Genética Baseado em Estratégias Evolucionárias. MSc Thesis, Universidade Federal do Paraná, Curitiba, Brasil. [publication]Sara Silva and Costa, E. , "Comparing Tree Depth Limits and Resource-Limited GP", in 2005 IEEE Congress on Evolutionary Computation, 2005 [citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39. [citation][year=2009]Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK. [citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza). [citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia. 2004(6 publications) [publication]Tavares, J. and Penousal Machado and Amilcar Cardoso and Pereira, F.B. and Costa, E. , "On the Evolution of Evolutionary Algorithms", in 7th European Conference on Genetic Programming, 2004 [citation][year=2012]Soghier, Amr. "Novel Hyper-heuristic Approaches in Exam Timetabling." (2012). [citation][year=2011]Spector, Lee, "Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems", Genetic Programming Theory and Practice VIII, pp. 17-33, 2011, Isbn: 978-1-4419-7747-2 Doi: 10.1007/978-1-4419-7747-2_2 [citation][year=2011]Algorithms and data structures for three-dimensional packing SD Allen - 2011 - etheses.nottingham.ac.uk [citation][year=2010]Automatic Generation of Three-Dimensional Packing Heuristics SD Allen, EK Burke - 2010 - Technical report, University of … [citation][year=2010]T. Hu and W. Banzhaf. Evolvability and speed of evolutionary algorithms in light of recent developments in biology. Journal of Artificial Evolution and Applications, pages 1–28, 2010; [citation][year=2010]E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and R. Qu. Hyper-heuristics: A survey of the state of the art. Technical Report NOTTCS-TRSUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham, 2010; [citation][year=2010]M. Hyde. A Genetic Programming Hyper-Heuristic Approach to Automated Packing. PhD thesis, University of Nottingham, March 2010; [citation][year=2010]L. Spector. Towards practical autoconstructive evolution: Self-evolution of problem-solving genetic programming systems. Genetic Programming Theory and Practice VIII, 8:17–33, 2010 [citation][year=2009]Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Ozcan and Rong Qu, \textbf{A Survey of Hyper-heuristics}, School of Computer Science and Information Technology University of Nottingham, Computer Science Technical Report No. NOTTCS-TR-SUB-0906241418-2747, March 2009. [citation][year=2009]Evolution of Search Algorithms Using Graph Structured Program Evolution, Shinichi Shirakawa and Tomoharu Nagao, Proceedings of EuroGP 2009, L. Vaneschi et alli (Eds.), LNCS 5481, pp. 109-120. [citation][year=2007]George G. Mitchell, Evolutionary Computation Applied to Combinatorial Optimisation Problems, Ph.D. Thesis, School of Electronic Engineering, Dublin City University, September, 2007. [citation][year=2007]S. Shirakawa and T. Nagao. Evolution of sorting algorithm using graph structured program evolution. In SMC, pages 1256–1261. IEEE, 2007; [citation][year=2006]Gautham Anil. On an alternative approach to Evolutionary Programming, M.Tech Thesis, Jul 2006. [citation][year=2006]Laura Diosan and Mihai Oltean, Evolving crossover operators for function optimization, in Proceedings of the European Conference on Genetic Programming (EuroGP2006), P. Collet et al (Eds.), LNCS 3905, pp. 97-108, Springer-Verlag, 2006. [citation][year=2006]Laura Diosan and Mihai Oltean, Evolving the structure of the particle swarm optimization algorithms, in Proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization (EuroGP2006), J. Gottlieb and G. Raidl (Eds.), LNCS 3906, pp. 25-36, Springer-Verlag, 2006. [citation][year=2005]Jose Antonio Martin H., Search space modulation in genetic algorithms: evolving the search space by sinusoidal transformations, In Proceedings of the Genetic and Evolutionary Computation Conference, Washinghton D.C., USA, 25-29 June, 2005. [citation][year=2005]Mihai Oltean, Evolving Evolutionary Algorithms using Linear Genetic Programming, Evolutionary Computation, MIT Press, Vol. 13. Issue 3, 2005 [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Understanding the Role of Insertion and Correction in the Evolution of Golomb Rulers", in Congress on Evolutionary Computation, 2004 [citation][year=2008]\item Christophe Meyera, and Periklis A. Papakonstantinou, \textbf{On the complexity of constructing Golomb Rulers}, Discrete Applied Mathematics, Elsevier, August 2008. [citation][year=2004]C. Cotta, and A. J. Fernndez, A Hybrid GRASP - Evolutionary Algorithm Approach to Golomb Ruler Search, In Proceedings of the 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), Birmingham, UK, 18-22 September, 2004. [publication]Sara Silva and Costa, E. , "Dynamic Limits for Bloat Control - Variations on Size and Depth", in Genetic and Evolutionary Computation Conference (GECCO-2004), 2004 [citation][year=2011]Neshatian K, Zhang M (2011). Using genetic programming for context-sensitive feature scoring in classification problems. Connection Science 23(3): 183-207. [citation][year=2011]Bhardwaj A, Sakalle A, Chouhan H, Bhardwaj H (2011). Controlling the problem of bloating using stepwise crossover and double mutation technique. Advanced Computing: An International Journal (ACIJ), 2(6): 59–68. [citation][year=2011]Selle B, Muttil N (2011). Testing the structure of a hydrological model using Genetic Programming. Journal of Hydrology 397(1–2): 1–9. [citation][year=2010]Beikia M, Basharib A, Majdia A (2010). Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. International Journal of Rock Mechanics and Mining Sciences, 47(7): 1091--1103. [citation][year=2009]Amil NM, Bredeche N, Gagné C, Gelly S, Schoenauer M, Teytaud O (2009). A Statistical Learning Perspective of Genetic Programming. In Proc 12th European Conference on Genetic Programming (EuroGP) at EvoStar 2009, 327"338. [citation][year=2009]Kumar R, Bal BK, Rockett PI (2009). Multiobjective Genetic Programming Approach to Evolving Heuristics for the Bounded Diameter Minimum Spanning Tree Problem. In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 309"316. [citation][year=2009]Neshatian K, Zhang M (2009). Pareto Front Feature Selection: Using Genetic Programming to Explore Feature Space. In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 1027"1034. [citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39. [citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza). [citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1"6. [citation][year=2008]Neshatian K, Zhang M (2008). Genetic Programming and Class-Wise Orthogonal Transformation for Dimension Reduction in Classification Problems. In Proc 11th European Conference on Genetic Programming (EuroGP) at EvoStar 2008, 242"253. [citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia. [citation][year=2007]Neshatian K, Zhang M, Johnston M (2007). Feature Construction and Dimension Reduction Using Genetic Programming. In Proc 20th Australian Conference on Artificial Intelligence, 160"170. [citation][year=2007]Parasuramana K, Elshorbagya A, Sib BC (2007). Estimating Saturated Hydraulic Conductivity Using Genetic Programming. Soil Science Society America Journal, 71: 1676"1684. [citation][year=2007]Parasuraman K (2007). Hydrologic Prediction Using Pattern Recognition and Soft-Computing Techniques. PhD Thesis, Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, Canada. [citation][year=2006]Luke S, Panait L (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation 14(3): 309"344. [citation][year=2006]Gelly S, Teytaud O, Bredeche N, Schoenauer M (2006). Universal Consistency and Bloat in GP. Revue d"Intelligence Artificielle 20(6): 805"827. [citation][year=2006]Shuhua Z, Qian G, Jianguo S (2006). Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining. Journal of China University of Geosciences 17(4): 361"366. [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Evolving Golomb Rulers", in Genetic and Evolutionary Computation Conference (GECCO-2004), 2004 [citation][year=2004]H. Mauch, Closest Substring Problem - Results from an Evolutionary Algorithm, In Proceedings of the 11th International Conference of Neural Information Processing (ICONIP 2004), Lecture Notes in Computer Science, Volume 3316, pp. 205"211, Calcutta, India, November 22-25, 2004. [publication]Penousal Machado and Tavares, J. and Amilcar Cardoso and Pereira, F.B. and Costa, E. , "Evolving Creativity", in Computational Creativity Workshop, 7th European Conference in Case Based Reasoning, 2004 [citation][year=2015]Turn-based evolution in a simplified model of artistic creative process P Dahlstedt - Evolutionary Intelligence, 2015 - Springer [citation][year=2010]Sorenson, Nathan, and Philippe Pasquier. "The evolution of fun: Automatic level design through challenge modeling." Proceedings of the First International Conference on Computational Creativity (ICCCX). Lisbon, Portugal: ACM. 2010. [citation][year=2007]Uta Hellinger, Computational support of human creativity in design, MSc. Thesis, Laboratoire d"informatique Grenoble - Equipe MAGMA, Institut fur Algorithmen und kognitive Systeme, University of Karlsruhe (TH), July, 2007. [publication]Brabazon, A. and Silva, A. and Sousa, T. and O'Neill, M. and Matthews, R. and Costa, E. , "A Particle Swarm Model of Organizational Adaptation", in Genetic and Evolutionary Computation Conference (GECCO-2004), vol. 3102, 2004 [citation][year=2007]Xiaohui Cui and Thomas E. Potok (2007): A Particle Swarm Social Model for Multi-Agent Based Insurgency Warfare Simulation. Fifth International Conference on Software Engineering Research, Management and Applications. pp. 177-183, IEEE Press, 2007. [citation][year=2005]Far from equilibrium computation and particle swarm optimization, L. diosan, D. Dumitrescu and D. David, Acta Universitatis Apulensis, nº10/2005. 2003(9 publications) [publication]Tavares, J. and Pereira, F.B. and Penousal Machado and Costa, E. , "On the Influence of GVR in Vehicle Routing", 2003 [citation][year=2015]Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using Google Maps API A Király, J Abonyi - Engineering Applications of Artificial Intelligence, 2015 - Elsevier [citation][year=2015]New Notation and Classification Scheme for Vehicle Routing Problems WR Cherif-Khettaf, MH Rachid, C Bloch… - RAIRO-Operations …, 2015 - rairo-ro.org [citation][year=2015]Sequential insertion heuristic with adaptive bee colony optimisation algorithm for vehicle routing problem with time windows S Jawarneh, S Abdullah - PloS one, 2015 - journals.plos.org [citation][year=2011]B. Yu, Z.Z. Yang, B.Z. Yao, A hybrid algorithm for vehicle routing problem with time windows, Expert Systems with Applications, Volume 38, Issue 1, January 2011, Pages 435-441, ISSN 0957-4174, 10.1016/j.eswa.2010.06.082. [citation][year=2010]N. Mukai, T. Wanatabe, Route optimization using evolutionary approaches for on-demand pickup problem, in Int. J. Advanced Intelligence Paradigms Vol. 2, Nº 1, 2010. [citation][year=2010]Naoto Mukai and Kosuke Kawamura, "Simulation evaluation for on-demand bus system with electrical vehicles", Intelligent Decision Technologies, Vol.4, N. 4, pp.307-314, 2010, DOI - 10.3233/IDT-2010-0092 [citation][year=2009]Affenzeller, Winkler, Wagner, Beham, \textbf{Genetic Algorithms and Genetic Programming Modern Concepts and Practical Applications}, Hard Cover / 379 pages B&W, Chapman & Hall/CRC; 1 edition (April 9th, 2009) [citation][year=2009]Garcia-Najera, A. & Bullinaria, J.A. (2009). \textbf{Bi-objective Optimization for the Vehicle Routing Problem with Time Windows: Using Route Similarity to Enhance Performance}. In: M. Ehrgott, C. Fonseca, X. Gandibleux, J.K. Hao & M.Sevaux (Eds), Proceedings of the Fifth International Conference on Evolutionary Multi-Criterion Optimization (EMO'09), 275-289. Berlin: Springer-Verlag. [citation][year=2009]JY Potvin. State-of-the Art Review—Evolutionary Algorithms for Vehicle Routing. INFORMS Journal on Computing, 2009. [citation][year=2009]JY Potvin. A review of bio-inspired algorithms for vehicle routing. Bio-inspired Algorithms for the Vehicle Routing, Studies in Computational Intelligence, Springer, 2009. [citation][year=2009]JE Mendoza, AL Medaglia, N Velasco. An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems, 2009. [citation][year=2009]Anna I. Esparcia-Alcázar, Manuel Cardós, J. J. Merelo, Anaís Martínez-García, Pablo García-Sánchez, Eva Alfaro-Cid, Ken Sharman. EVITA: An integral evolutionary methodology for the inventory and transportation problem. Bio-inspired Algorithms for the Vehicle Routing, Studies in Computational Intelligence, Springer, 2009. [citation][year=2009]Z Ursani, D Essam, D Cornforth, R Stocker. Introducing the localized genetic algorithm for small scale capacitated vehicle routing problems. INFOR: Information Systems and Operational Research, 2009. [citation][year=2009]MS Sanders Jr . Solving the Vehicle Routing Problem with Multiple Multi-Capacity Vehicles. Computer, 2009. [citation][year=2009]K Kawamura, N Mukai. Optimization of Transport Plan for On-Demand Bus System Using Electrical Vehicles. Knowledge-Based and Intelligent Information and Engineering Systems, LNCS, 2009 - Springer. [citation][year=2009]N Mukai, N Ishii. R-Tree Based Path Representation for Vehicle Routing Problem. Tools with Artificial Intelligence, IEEE Press, 2009. [citation][year=2008] A. J. Pohl and G. B. Lamont. Multi-objective uav mission planning using evolutionary computa- tion. In S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, and J. W. Fowler, editors, Winter Simulation Conference, pages 1268–1279. WSC, 2008; [citation][year=2008] J.-Y. Potvin. A review of bio-inspired algorithms for vehicle routing. In F. Pereira and J. Tavares, editors, Bio-inspired Algorithms for the Vehicle Routing Problem, volume 161 of Studies in Computati- onal Intelligence, pages 1–34. Springer Berlin / Heidelberg, 2009. 10.1007/978-3-540-85152-3-1; [citation][year=2008] A. G. Najera. A first multi-objective genetic algorithm approach to solving the vehicle routing problem with time windows. Technical report, School of Computer Science, University of Birmingham, February 2008; [citation][year=2008] P. J. Yongzhong Wu and T. Wang. An empirical study of a pure genetic algorithm to solve the capacitated vehicle routing problem. ICIC Express Letters ICIC International, 2(1), March 2008; [citation][year=2008] J.-Y. Potvin. A review of bio-inspired algorithms for vehicle routing. Technical Report CIRRELT- 2008-30, Inter university Research Centre on Enterprise Networks, Logistics and Transportation, July 2008; [citation][year=2008] A. J. Pohl. Multi-objective uav mission planning using evolutionary computation. Master’s thesis, Department of the Air Force, Air University, Air Force Institute of Technology, Wright- Patterson Air Force Base, 2008; [citation][year=2008] H. Yildiz. Methodologies and Applications for Scheduling, Routing & Related Problems. PhD thesis, Tepper School of Business, Carnegie Mellon University, 2008; [citation][year=2008] A. Garcia-Najera and J. A. Bullinaria. Bi-objective optimization for the vehicle routing problem with time windows: Using route similarity to enhance performance. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, EMO, volume 5467 of Lecture Notes in Computer Science, pages 275–289. Springer, 2009; [citation][year=2008] A. Esparcia-Alcázar, M. Cardós, J. J. M. Guervós, A. Martínez-García, P. García-Sánchez, E. Alfaro-Cid, and K. Sharman. Evita: An integral evolutionary methodology for the inven- tory and transportation problem. In F. B. Pereira and J. Tavares, editors, Bio-inspired Algorithms for the Vehicle Routing Problem, volume 161 of Studies in Computational Intelligence, pages 151–172. Springer, 2008; [citation][year=2007]Josep Maria Salanova Grau, \textbf{Criteris de localització de terminals de consolidació en empreses de paqueteria}, Thesis, Departament d'Infraestructura del Transport i del Territori, Universitat Politècnica de Catalunya, 2007. [citation][year=2007]Jean-Yves Potvin, Evolutionary Algorithms for Vehicle Routing, Technical Report CIRRELT-2007-48, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), November, 2007. [citation][year=2007]Abel Garcia Najera, Population-based techniques for multi-ob jective optimization, Technical Report, School of Computer Science, University of Birmingham, August 2007. [citation][year=2007]Abel Garcia Najera, Two Natural Ways to Solve the Capacitated Vehicle Routing Problem, Technical Report, School of Computer Science, University of Birmingham, February, 2007. [citation][year=2007]Abel Garcia Najera, A Genetic Algorithm for the Capacitated Vehicle Routing Problem, Technical Report, School of Computer Science, University of Birmingham, February, 2007. [citation][year=2006]Kubiak M., Analysis of distance between vehicle routing problem solutions generated by memetic algorithms, In Proceedings of the 9th National Conference on Evolutionary Computation and Global Optimization, Murzasichle, Poland, 2006. [citation][year=2006]Guillermo González Vargas, and Felipe González Aristizábal, "Metaheuristics applied to vehicle routing. A case study. Parte 1: formulating the problem", Revista Ingeniería e Investigación, vol.26 no.3, Bogotá, December 2006. [citation][year=2006]K. C. Tan, Y. H. Chew, and L. H. Lee, A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows, In Journal of Computational Optimization and Applications, Kluwer, 2006. [citation][year=2005] C.-H. Tseng. Theory and implementation of an intelligent vehicle dispatching system. Master’s thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2005; [citation][year=2005] M. A. Russell and G. B. Lamont. A genetic algorithm for unmanned aerial vehicle routing. Proceedings of the 2005 conference on Genetic and evolutionary computation GECCO 05, page 1523, 2005; [citation][year=2005] J. Schönberger. Operational Freight Carrier Planning: Basic Concepts, Optimization Models and Advanced Memetic Algorithms. Springer, 2005; [citation][year=2005] K. C. Tan, E. F. Khor, and T. H. Lee. Multiobjective Evolutionary Algorithms and Applications. Springer-Verlag, United Kingdom, 2005; [citation][year=2005] H. Y. Michael P. Johnson, Stephen F. Roehrig. A genetic algorithm for the home-delivered meals location-routing problem. In IFORS Conference, Hawaii, July 2005; [citation][year=2005] Y.-L. Xu, M.-H. Lim, and M.-J. Er. Investigation on genetic representations for vehicle routing problem. In Systems, Man and Cybernetics, 2005 IEEE International Conference on, volume 4, pages 3083 – 3088 Vol. 4, oct. 2005; [citation][year=2005] P. Barros. Itrans - 3d transit simulator. Master’s thesis, Federal University of Pernambuco,Recife, 2005; [citation][year=2005] M. Russel. A genetic algorithm for uav routing integrated with a parallel swarm simulation. Master’s thesis, Department of the Air Force Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, 2005; [citation][year=2005]A. G. Qureshi. Analysis of the Effects of Cooperative Delivery System in Bangkok. PhD thesis, SCE :School of Civil Engineering, Asian Institute of Technology, Klong Luang, Thailand, 2005; [citation][year=2004]Wangzu Pillar, Cheng Ka-hing, Fang Hong, and Qian Fu Lan, An Hybrid Optimization Algorithm Solving Vehicle Routing Problems, Operations Research and Management Science, Vol.13 No.6 P.48-52, 2004. [citation][year=2004]A. Tighe, F. S. Smith and G. Lyons. Priority Based Solver for a Real-Time Dynamic Vehicle Routing, In Proceedings of the 2004 IEEE International Conference on Systems Man and Cybernetic (SMC 2004), The Hague, The Netherlands, 10-13 October, 2004. [citation][year=2004]R. J. Carmo, Uma Analise da Eficiencia dos Algoritmos Geneticos no Roteamento de Ve?culos, Bachelor Thesis, Universidade do Estado da Bahia, Brasil, Maio, 2004. [citation][year=2003]Wei-Che Chuang, An Inheritable Heuristic Algorithm for Bi-criteria Vehicle Routing Optimization Problems with Time Windows, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2003. [publication]Tavares, J. and Pereira, F.B. and Penousal Machado and Costa, E. , "Crossover and Diversity: A Study about GVR", in a bird-of-a-feather workshop at the 2003 Genetic and Evolutionary Computation Conference (GECCO-2003), 2003 [citation][year=2015]Genetic Algorithm Approach for a Class of Multi-Criteria, Multi-Vehicle Planner of UAVs E Freitas, JRH Carvalho - Evolutionary Multi-Criterion Optimization, 2015 - Springer [citation][year=2012]K Pulji?. An evolutionary algorithm based on repeated mutations for solving the capacitated vehicle routing problem. Journal of Computing and Information Technology, 2012. [citation][year=2010]M. Chavez, O. Parra, Evolutionary algorithm for the vehicles routing problem with time windows, in Computation Y Sistemas, Vol. 13, nº 3, pp. 257-272, 2010. [citation][year=2010]Neil Urquhart, Emma Hart and Cathy Scott (2010). Building low CO2 solutions to the Vehicle Routing Problem with Time Windows using an Evolutionary Algorithm. WCCI 2010 IEEE World Congress on Computational Intelligence - IEEE CEC, pp. 3161-3168, July, 18-23, 2010 - CCIB, Barcelona, Spain, IEEE, 2010. [citation][year=2010]Naoto Mukai and Kosuke Kawamura, "Simulation evaluation for on-demand bus system with electrical vehicles", Intelligent Decision Technologies, Vol.4, N. 4, pp.307-314, 2010, DOI - 10.3233/IDT-2010-0092 [citation][year=2009]Ferdinand de Bakker, \textbf{Phoenix " Non-cooperative bargaining agents}, Master of Media and Knowledge Engineering, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, The Netherlands, August 2009. [citation][year=2009]JY Potvin. State-of-the Art Review—Evolutionary Algorithms for Vehicle Routing. INFORMS Journal on Computing, 2009. [citation][year=2009]JE Mendoza, AL Medaglia, N Velasco. An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems, 2009 - Elsevier. [citation][year=2009]Z Ursani, D Essam, D Cornforth, R Stocker. Introducing the localized genetic algorithm for small scale capacitated vehicle routing problems. INFOR: Information Systems and Operational Research, 2009. [citation][year=2008]Tadahiko Murata, and Ryota Itai, \textbf{Enhancing Solution Similarity in Multi-Objective Vehicle Routing Problems with Different Demand Periods}, In Vehicle Routing Problem, Tonci Caric and Hrvoje Gold (Editors), Chapter 7, In-Teh Publishers, September 2008, ISBN 978-953-7619-09-1 [citation][year=2008]H. Sallam, C. S. Regazzoni, I. Talkhan, and A. Atiya, Measuring the Genotype Diversity of Evolvable Neural Networks, In Proceedings of the Sixth International Conference INFOS2008, Egypt, 27-28 March, 2008. [citation][year=2008]H. Sallam, C. S. Regazzoni, I. Talkhan and A. Atiya (2008). The Effect of Genetic Operations on the Diversity of Evolvable Neural Networks. IADIS International Conference Intelligent Systems and Agents 2008, pp. 143-150. [citation][year=2008]Abel Garcia Najera, \textbf{A First Multi-ob jective Genetic Algorithm Approach To Solving The Vehicle Routing Problem With Time Windows}, Technical Report, School of Computer Science, University of Birmingham, February 2008. [citation][year=2007]Jean-Yves Potvin, Evolutionary Algorithms for Vehicle Routing, Technical Report CIRRELT-2007-48, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), November, 2007. [citation][year=2007]Jorge E. Mendoza, Andres L. Medaglia, and Nubia M. Velasco, "An evolutionary based decision support system for vehicle routing: the case of a public utility", Departamento de Ingenier?a Industrial, Universidad de los Andes, COPA 2007-1, September 2007. [citation][year=2007]Abel Garcia Najera, Population-based techniques for multi-ob jective optimization, Technical Report, School of Computer Science, University of Birmingham, August 2007. [citation][year=2007]Abel Garcia Najera, A Genetic Algorithm for the Capacitated Vehicle Routing Problem, Technical Report, School of Computer Science, University of Birmingham, February, 2007. [citation][year=2007]Tadahiko Murata and Ryota Itai (2007). Local Search in Two-Fold EMO Algorithm to Enhance Solution Similarity for Multi-objective Vehicle Routing Problems. In Evolutionary Multi-Criterion Optimization, pp. 201-215, LNCS Volume 4403, Springer Berlin/Heidelberg, 2007. [citation][year=2006]Guillermo González Vargas, and Felipe González Aristizábal, "Metaheuristics applied to vehicle routing. A case study. Parte 1: formulating the problem", Revista Ingeniería e Investigación, vol.26 no.3, Bogotá, December 2006. [citation][year=2006]Oliver Kunze, Tourenplanung für den eCommerce-Lebensmittel-Heimlieferservice, PhD Thesis, Fakultät für Maschinenbau, Universität Karlsruhe, Germany, March, 2006. [citation][year=2005]Cheng-Hung Tseng, Theory and Implementation of an Intelligent Vehicle Dispatching System, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2005. [citation][year=2005]Tadahiko Murata1 and Ryota Itai1, Multi-objective Vehicle Routing Problems Using Two-Fold EMO Algorithms to Enhance Solution Similarity on Non-dominated Solutions, Evolutionary Multi-Criterion Optimization: Third International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005. [citation][year=2005]M. Russel, and G. Lamont A Genetic Algorithm for Unmanned Aerial Vehicle Routing, In Proceedings of the Genetic and Evolutionary Computation Conference, Washinghton D.C., USA, 25-29 June, 2005. [citation][year=2005]M. Russel, A Genetic Algorithm for UAV Routing Integrated with a parallel Swarm Simulation, Master Thesis, Department of the Air Force Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, May, 2005. [citation][year=2003]Wei-Che Chuang, An Inheritable Heuristic Algorithm for Bi-criteria Vehicle Routing Optimization Problems with Time Windows, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2003. [publication]Simões, A. and Costa, E. , "An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory", 2003 [citation][year=2015]Dragoi, E. N., Curteanu, S., Cascaval, D., & Galaction, A. I. (2015). SEPARATION OF SUCCINIC ACID FROM FERMENTATION BROTHS. MODELLING AND OPTIMIZATION. Environmental Engineering and Management Journal, 14(3), 533-539. [citation][year=2015]Qian, S., Ye, Y., Jiang, B., & Wang, J. (2015). Constrained Multiobjective Optimization Algorithm Based on Immune System Model. [citation][year=2014]Li, Z., Li, Y., Kuang, L., & Yu, F. (2014, July). Artificial immune system application for solving dynamic optimization problems. In Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 2906-2911). IEEE. [citation][year=2014]Abello, M. B. (2014). Application of Memory-Based Approach to Multi-Objective Optimisation on Dynamic Resource-Constrained Project Scheduling with Time-varying Number of Tasks (Doctoral dissertation, University of Adelaide). [citation][year=2013]Weiwei Zhang, and Gary G. Yen. "A quasi-gradient and cluster-based artificial immune system for dynamic optimization.", 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2306-2313, IEEE, 2013. [citation][year=2013]Wuhui Hong, Qian Shu, Qu Xuzhi Dan (2013). Clonal selection immune genetic algorithm for high dimensional 0/1 knapsack problem applications, Computer Applications, Number 3, pp. 845-848, 2013. [citation][year=2013]Halder, U., Das, S., & Maity, D. (2013). A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments. Cybernetics, IEEE Transactions on, 43(3), 881-897. [citation][year=2013]Yang, S., Jiang, Y., & Nguyen, T. T. (2013). Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 24(4), 451-480. [citation][year=2013]Nguyen, T. T., Yang, S., Branke, J., & Yao, X. (2013). Evolutionary dynamic optimization: methodologies. In Evolutionary Computation for Dynamic Optimization Problems (pp. 39-64). Springer Berlin Heidelberg. [citation][year=2013]Qian, S. Q., & Wu, H. H. (2013, January). Dynamic immune optimization algorithm for Knapsack problem in dynamic environments. In Conference Anthology, IEEE (pp. 1-4). IEEE. [citation][year=2013]Mavrovouniotis, M. (2013). Ant Colony Optimization in Stationary and Dynamic Environments (Doctoral dissertation, University of Leicester). [citation][year=2013]Lutao, W. A. N. G. (2013). Genetic network programming based rule accumulation for agent control. [citation][year=2013]Shu-Qu, Q., & Hui-Hong, W. (2013, August). Dynamic Stochastic Ranking Selection Immune Optimization Algorithm for Dynamical 0/1 Knapsack Problem. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on (Vol. 1, pp. 100-103). IEEE. [citation][year=2013]Khalid, J. H. (2013). Modèles évolutifs flous pour l'optimisation dynamique. [citation][year=2012]Udit Halder, Swagatam Das, and Dipnakar Maity. A Cluster-Based Differential Evolution Algorithm With External Archive for Optimization in Dynamic Environments. IEEE Transactions on Cybernetics, pp. 1-17, IEEE, 2012. [citation][year=2012]Lili Liu, Dingwei Wang, and Jiafu Tang (2012). An immune system based differential evolution algorithm using near-neighbor effect in dynamic environments. Journal of Control Theory and Applications 10.4, pp. 417-425, Springer, 2012. [citation][year=2012]T. T. Nguyen, S. Yang, and J. Branke (2012). “Evolutionary dynamic optimization: A survey of the state of the art”. Swarm and Evolutionary Computation, Elsevier, 2012. [citation][year=2012]Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2012). "Metaheuristics for dynamic combinatorial optimization problems." IMA Journal of Management Mathematics, 2012. [citation][year=2011]Shi Xu-hua, Qian Feng (2011). Immune response-based Algorithm for Optimization of Dynamic Environments. Journal of Cent. South University Technology (18), pp. 1563-1571, Springer 2011. [citation][year=2011]Carlos Cruz, Juan R. González, David Pelta (2011). Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing, 15 (7), Springer-Verlag, 2011. [citation][year=2011]Fengming Ye (2011). A study on the memory schemes for genetic network programming. PhD Thesis, Waseda University, Japan, 2011. [citation][year=2011]Chen Li (2011). Dynamic Optimization Algorithms. Journal of Wuhan University: Natural Science, 2011. [citation][year=2011]Quian Shuqu, Wu Huihong (2011). Dynamic Immune Algorithm Based on Simulated Annealing Selection and its Application. Journal of Computer Engineering and Applications, 47(36), pp. 57-60, 2011. [citation][year=2011]Ju Shangyou (2011). Dynamic diffusion particle swarm algorithm and its application. Journal of Computer Engineering and Applications 47(36), pp. 61.64, 2011. [citation][year=2010]Z. Zhang and S. Qian, Immune algorithm with dynamic environmetyns and its application to grenhouse control, Journal of Optimization and engineering, vol. 11, nº 1, pp. 125-144, 2010. [citation][year=2010]Deng Chang-shou, Angle modulation differential evolution with dual population for high dimension 0-1 knapsack problem. Computer Engineering and Applications, 2010, 46 (24), pp. 45-47, 2010. [citation][year=2010]Carlos Cruz, Juan R. González and David A. Pelta (2010). Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing - A Fusion of Foundations, Methodologies and Applications, pp. 1-22, Springer, 2010. [citation][year=2009]I. Dempsey, M. O´Neill, A. Brabazon (2009), Foundations in Grammatical Evolution for Dynamic Environments, Studies in Computational Intelligence, Vol. 194 , Springer-Verlag, 2009. [citation][year=2009]L. Liu, D. Wang, S. Yang (2009), ?An Immune System Based Genetic Algorithm Using Permutation-based Dualism for Dynamic Traveling Salesman Problems?. In M. Giacobini et al. (Eds.): EvoWorkshops 2009, Applications of Evolutionary Computing, LNCS 5484, pp. 725"734, Springer Verlag, 2009. [citation][year=2009]Alexander V. Spirov, David M. Holloway (2009). "Design of a dynamic model of genes with multiple autonomous regulatory modules by evolution in silico?. Nature Precedings : doi:10.1038/npre.2009.3913.1, 2009. [citation][year=2009]Xin Yu, Ke Tang, Tianshi Chen, Xin Yao (2009). Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Journal of Memetic Computing, Vol. 1, 1, pp. 3-24, Springer 2009. [citation][year=2009]Z. Zhang, S. Qian (2009). Immune algorithm with antibody"repaired and its application for high"dimensional 0/ 1 knapsack problem. ApplicationResearch ofComputer, number 8, vol 26, pp. 2921 - 2939. [citation][year=2008]Shengxiang Yang (2008). "Genetic Algorithms with Memory and Elitism-Based Immigrants in Dynamic Environments?. Evolutionary Computation, Vol. 16, No. 3, pp. 385-416, MIT Press, Fall 2008. [citation][year=2008]Shengxiang Yang, X. Yao (2008). "Population-based incremental learning with associative memory for dynamic environments?. IEEE Transactions on Evolutionary Computation, 12(5): 542-561, IEEE Press, October 2008. [citation][year=2008]Shengxiang Yang, Renato Tinos (2008). "Hyper-selection in dynamic environments?. Proceedings of the IEEE Congress on Evolutionary Computation, 2008. CEC 2008, pp. 3185 " 3192, IEEE Press 2008. [citation][year=2008]Xin Yu, Ke Tang, Tianshi, Xin Yao (2008). "Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization?. Journal Memetic Computing , Springer, 2008. [citation][year=2008]Mehdi Azimipour, Mohammad Reza Bonyadi, Mohammad Eshghi (2008). Using Immune Genetic Algorithm in ATPG. Australian Journal of Basic and Applied Sciences, 2(4), pp. 920-928, INSInet Publication, 2008. [citation][year=2008]Jingchen Liu, Xiaogang Zang, Xinbao Gong (2008). "Immune System Assisted Radial Basis Function Network for OFDM System Channel Tracking in Dynamic Environments". Eighth International Conference on Intelligent Systems Design and Applications, vol. 1, pp.582-586, IEEE, 2008. [citation][year=2007]Shengxiang Yang and Renato Tinós (2007). A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing, Volume 4, Number 3, pp. 243-254, Science Press, co-published with Springer-Verlag, 2007. [citation][year=2007]Shengxiang Yang (2007): "Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems?. In M. Giacobini et al. (Eds.): EvoWorkshops 2007, Applications of Evolutionary Computing, LNCS 4448, pp. 627-636, Springer, 2007. [citation][year=2007]Nikolaos Nanas, Anne De Roeck (2007): ?Multimodal Dynamic Optimization: From Evolutionary Algorithms to Artificial Immune Systems?. Artificial Immune Systems, LNCS 4628, pp. 13-24, Sprionger, 2007. [citation][year=2007]Adnan Acan, Ahmet Unveren (2007), "A shared-memory ACO+GA hybrid for combinatorial optimization?. IEEE Congress on Evolutionary Computation, 2007. CEC 2007, pp. 2078-2085, IEEE 2007. [citation][year=2006]Shengxiang Yang (2006), A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments, Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (GECCO 2006), pp 1377"1384, Seattle, Washington, USA, 2006. [citation][year=2006]Shengxiang Yang (2006), Associative Memory Scheme for Genetic Algorithms in Dynamic Environments, Proceedings of the 2006 EvoWorkshops " EvoSTOC 2006, pp. 788"799, Budapest, Hungary, Applications of Evolutionary Computation, LNCS 3907, Springer, 2006. [citation][year=2006]Muhammad Rozi Malim, Ahamad Tajudin Khader, Adli Mustafa (2006), Artificial Immune Algorithms for University Timetabling. Edmund K. Burke, Hana Rudová (Eds.), Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2006), 30th August " 1st September, Brno, Czech Republic, 2006. [citation][year=2006]Lars Zwanepol Klinkmeijer (2006): A Serial Population Algorithm for Dynamic Optimization Problems. Concluding thesis for Cognitive Artificial Intelligence, Utrecht University, 2006. [citation][year=2006]Lars Zwanepol Klinkmeijer, Edwin de Jong, Marco Wiering (2006): A Serial Population Genetic Algorithm for Dynamic Optimization Problems. Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands, 2006. [citation][year=2006]Nikolaos Nanas , Anne de Roeck, Victoria Uren (2006): "Immune-Inspired Adaptive Information Filtering?. Artificial Immune Systems, LNCS 4163, pp. 418-431, Springer, 2006. [citation][year=2006]Shengxiang Yang (2006), "On the Design of Diploid Genetic Algorithms for Problem Optimization in Dynamic Environments?. 2006 IEEE Congress on Evolutionary Computation, pp. 1362-1369, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, 2006. [citation][year=2006]Muhammad Rozi Malim, Ahamad Tajudin Khader, Adli Mustafa (2006), "An Immune-Based Approach to University Course Timetabling: Negative Selection Algorithm?. Proceedings of the 2th IMT-GT Regional Conference on Mathematics, Statistics and Applications, University Sains Malaysia, Penang, June 13-15, 2006. [citation][year=2005]Adnan Acan, Yüce Tekol (2005). Performance-based Computation of Chromosome Lifetimes in Genetic Algorithms. In Knowlodge Incorporation in Evolutionary Computation, Y. Jin (Editor), pp. 195-213, Studies in Fuzziness and Soft Computing, Springer, 2005. [citation][year=2004]Alexander V. Spirov (2004), Retroviral GA and Fitness Functions with Subbasin-Portal Architecture, Proceedings of the 6th Annual Conference on Genetic and Evolutionary Computation (GECCO 2004), Seattle, Washington USA, 25-30 June 2004. [publication]Simões, A. and Costa, E. , "Improving the Genetic Algorithm's Performance when Using Transformation", 2003 [citation][year=2015]Bravo, Y., Luque, G., & Alba, E. (2015). Global memory schemes for dynamic optimization. Natural Computing, 1-15. [citation][year=2014]Palanisamy, V. (2014). Enhanced Technique To Improve The Performance of Genetic Algorithm. Australian Journal of Basic and Applied Sciences, 8(17), 447-453. [citation][year=2014]Lakshmi, R., & Vivekanandan, K. (2014). A Novel Methodology for Genetic Algorithms in Crossover Operation: Segment Replacement Operator. International Journal of Innovative Research and Development, 3(1). [citation][year=2013]Wuhui Hong, Qian Shu, Qu Xuzhi Dan (2013). Clonal selection immune genetic algorithm for high dimensional 0/1 knapsack problem applications, Computer Applications, Number 3, pp. 845-848, 2013. [citation][year=2011]S. Siva Sathya, S. Kuppuswami (2011). Gene silencing—A genetic operator for constrained optimization. Applied Soft Computing, Volume 11, Issue 8, pp. 5801–5808, Elsevier 2011. [citation][year=2011]S. Siva Sathya, S. Kuppuswami, S. Sendhil Kumar (2011). Gene Silencing Genetic Algorithm for 0/1 Knapsack with Object Preferences. International Journal of Computational Intelligence Systems, Volume 4, Issue 5, pp. 886-893, Taylor&Francis;, 2011. [citation][year=2010]Deng Chang-shou, Angle modulation differential evolution with dual population for high dimension 0-1 knapsack problem. Computer Engineering and Applications, 2010, 46 (24), pp. 45-47, 2010. [citation][year=2009]Z. Zhang, S. Qian (2009). Immune algorithm with antibody"repaired and its application for high"dimensional 0/ 1 knapsack problem. ApplicationResearch ofComputer, number 8, vol 26, pp. 2921 - 2939. [citation][year=2008]Shengxiang Yang (2008). "Genetic Algorithms with Memory and Elitism-Based Immigrants in Dynamic Environments?. Evolutionary Computation, Vol. 16, No. 3, pp. 385-416, MIT Press, Fall 2008. [citation][year=2008]Shengxiang Yang, X. Yao (2008). "Population-based incremental learning with associative memory for dynamic environments?. IEEE Transactions on Evolutionary Computation, 12(5): 542-561, IEEE Press, October 2008. [citation][year=2007]B. Dilimulati, I. Bruha (2007). Genetic Algorithms in a dynamically changing environment. Data Mining VIII: Data, Text and Web Mining and their Business Applications. Transaction: Information and Communication Technologies volume 38, WitPress, 2007. [citation][year=2006]Shengxiang Yang (2006), "A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments?, Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (GECCO 2006), pp 1377"1384, Seattle, Washington, USA, 2006. [publication]Simões, A. and Costa, E. , "A Comparative Study Using Genetic Algorithms to Deal with Dynamic Environments", 2003 [citation][year=2012]Lauren Davis, Funda Samanlioglu, Xiaochun Jiang, Daniel Mota, Paul Stanfield (2012). A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP). In Journal of Computers and Operations Research, Volume 39 Issue 1, January, Elsevier Science, 2012 [citation][year=2012]Lu Jiang-lin, He Zhong-shi, Chen Zi-Yu (2012). Discrete particle swarm optimization algorithm for solving dynamic knapsack problema. Computer Science Journal, Vol 39, N. 2, Chinese Science and Technology journals database, 2012. [citation][year=2009]Yang Yan, Dingwei Wang, Dazhi Wang, Hongfeng Wang (2009). Multi-Agent based Evolutionary Algorithm for Dynamic Knapsack Problem. Journal of Northeastern University, Natural Science, 2009. [citation][year=2008]Yang Yan, Dingwei Wang, Hongfeng Wang, Dazhi Wang (2008). Multi-Agent based Evolutionary Algorithm for Dynamic Knapsack Problem. 2008 Chinese Control and Decision Conference, IEEE, 2008. [citation][year=2006]Lars Zwanepol Klinkmeijer (2006): A Serial Population Algorithm for Dynamic Optimization Problems. Concluding thesis for Cognitive Artificial Intelligence, Utrecht University, 2006. [citation][year=2006]Lars Zwanepol Klinkmeijer, Edwin de Jong, Marco Wiering (2006): A Serial Population Genetic Algorithm for Dynamic Optimization Problems. Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands, 2006. [publication]Pereira, F.B. and Tavares, J. and Costa, E. , "Golomb Rulers: The Advantage of Evolution", in Workshop on Artificial Life and Evolutionary Algorithms (ALEAâ??03), 2003 [citation][year=2012]M Sorge, H Moser, R Niedermeier, M Weller. Exploiting a hypergraph model for finding golomb rulers. Combinatorial Optimization, 2012 - Springer. [citation][year=2009]C. Cotta, A. J. Fernandéz (2009). Solving Constrained Optimization Problems with Hybrid Evolutionary Algortihms. In Optimization Techniques for Solving Complex Problems, E. Alba et. al (Eds.), Wiley. [citation][year=2007]C. Cotta, I. Dotú, A.J. Fernández, P. Van Hentenryck, Local Search-Based Hybrid Algorithms for Finding Golomb Rulers, Constraints, Volume 12, Number 3, September, Springer Netherlands, 2007. [citation][year=2006]C. Cotta, I. Dotú, A. Fernández, P. Van Hentenryck, A Memetic Approach to Golomb Rulers, Parallel Problem Solving from Nature IX, T. Runarsson et al., Lecture Notes in Computer Science 4193, Springer-Verlag, Berlin Heidelberg, 2006. [citation][year=2006]I. Dotú (2006). Towards Hybrid Methods for Solving Hard Combinatorial Optimization Problems. Ph. D. Thesis, Universidad Autónoma de Madrid, Spain, September 2006. [citation][year=2005]Ivan Dotu and Pascal Van Hentenryck, A Simple Hybrid Evolutionary Algortihm for Finding Golomb Rulers, In Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC'05), Edimburgh, UK, September 2005. [citation][year=2005]Cotta, C; Fernadez, AJ Analysing fitness landscapes for the optimal Goulomb ruler problem. Proceedings of the 5th European Conference on Combinatorial Optimization, LNCS 3448, 68-79, 2005. [citation][year=2004]C. Cotta, and A. J. Fernndez, Analyzing Fitness Landscapes for the Optimal Golomb Ruler Problem, In Proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2005), Lausanne, Switzerland, 30 March - 1 April, 2005. [citation][year=2004]C. Cotta, and A. J. Fernndez, A Hybrid GRASP - Evolutionary Algorithm Approach to Golomb Ruler Search, In Proceedings of the The 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), Birmingham, UK, 18-22 September, 2004. [publication]Barreiros, J. and Costa, E. , "An Hierarchic Genetic Algorithm for Computing (near) Optimal Euclidean Steiner Trees", in Workshop on Application of Hybrid Evolutionary Algorithms to NP-Complete Problems (GECCO2003), 2003 [citation][year=2007]Ian Frommer and Bruce Golden (2007): A Genetic Algorithm for Solving the Euclidean Non-Uniform Steiner Tree Problem. Extending the Horizons: Advances in Computing, Optimization, and Decision Technologies, Operations Research/Computer Science Interfaces, Vol 37, pp. 31-48, Springer 2007. [citation][year=2006]Byounghak Yang (2006): Hybrid Evolutionary Algorithms for the Rectilinear Steiner Tree Problem Using Fitness Estimation. Computational Science and Its Applications - ICCSA 2006, LNCS 3982/2006, pp. 582-589, Springer 2006. [citation][year=2006]Byounghak Yang (2006): A Hybrid Evolutionary Algorithm for the Euclidean Steiner Tree Problem Using Local Searches. Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science 4251/2006, pp. 60-67, Springer 2006. [citation][year=2005]Ian Frommer, Bruce Golden, and Guruprasad Pundoor, "Heuristic Methods for Solving Euclidean Non-uniform Steiner Tree Problems", The Next Wave in Computing, Optimization, and Decision Technologies (B. Golden, S. Raghvan, and E. Wasil, eds.), Springer, 133-148, 2005. [citation][year=2005]MODELING AND OPTIMIZATION OF TRANSMISSION NETWORKS, Ian Frommer, Ph. D. Dissertation, University of Maryland, 2005 [citation][year=2005]Byounghak Yang (2005). A Hybrid Evolution Strategy on the Rectilinear Steiner Tree. DBPIA, pp. 27-37, 2005. [publication]Barreiros, J. and Costa, E. , "Global routing for lookup-table based FPGAs using genetic algorithms", in 13th International Conference on Field Programmable Logic and Applications 2003, 2003 [publication]Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "SAPPO: A Simple, Adaptive, Predator Prey Optimiser", in EPIA\'03, 2003 [citation][year=2009]M. Kathrada (2009). The flexi-PSO: Towards a more flexible particle swarm optimizer. OPSEARCH, Volume 46, Number 1, pp. 52-68, Springer 2009. [citation][year=2009]Xiaoxiang Liu, Weigang Jiang, Jianwen Xie (2009). A particle swarm optimization algorithm based on molecule diffusion. International Conference on Industrial Mechatronics and Automation, pp. 125 - 128, IEEE Press, 2009. [citation][year=2008]Weigang Jiang, Yuanbiao Zhang, Jianwen Xie (2008). A Particle Swarm Optimization Algorithm Based on Diffusion-Repulsion and Application to Portfolio Selection. 2008 International Symposium on Information, Science and Engieering, vol. 2, pp.498-501, 2008. [citation][year=2007]Mitsuharu Higashitani, Atsushi Ishigame, Keiichiro Yasuda, Pursuit-Escape Particle Swarm Optimization, IEEJ Transactions on Electrical and Electronic Engineering, Volume 3, Number 1, pp. 136-142. [citation][year=2006]Higashitani, M; Ishigame, A; Yasuda, K. Particle Swarm optimization considering the concpet of preadtor-prey bnehaviour. 2006 IEEE Congress on Evolutionary Computation, Vols. 1-6:434-437, 2006. [citation][year=2005]Salima Nabti, Souham Meshoul, and Mohamed Batouche, Predator Prey Optimizer for Unsupervised Clustering in Image Segmentation, International Arab Conference on Information Technology, ACIT'2005, December 6th- 8th, 2005, Al-Isra Private University, Jordan. [citation][year=2005]Volker Strunk, R�uber-Beute-Mechanismen zur Lenkung von Populationen in Evolution�ren Algorithmen, Diplomarbeit, Universit�t Dortmund, Fachbereich Informatik, April 2005. [citation][year=2005]Liu, BF; Chen, HM; Huang, HL; Hwang, SF; Ho, SY. Flexible ptrotein-ligand docking using particle swarm optimization. 2005 IEEE Congress on Evolutionary CXomputation, vols 1-3: 251-258, 2005. 2002(7 publications) [publication]Tavares, J. and Pereira, F.B. and Penousal Machado and Costa, E. , "GVR Delivers It On Time", 2002 [citation][year=2011]Ziauddin Ursani, Daryl Essam, David Cornforth, Robert Stocker, Localized genetic algorithm for vehicle routing problem with time windows, Applied Soft Computing, Volume 11, Issue 8, December 2011, Pages 5375-5390, ISSN 1568-4946 [citation][year=2010]Naoto Mukai and Kosuke Kawamura, "Simulation evaluation for on-demand bus system with electrical vehicles", Intelligent Decision Technologies, Vol.4, N. 4, pp.307-314, 2010, DOI - 10.3233/IDT-2010-0092 [citation][year=2010]— J. E. Mendoza, B. Castanier, C. Guéret, A. L. Medaglia, and N. Velasco. A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Computers & Operations Research, 37(11):1886–1898, 2010 [citation][year=2009]JY Potvin. State-of-the Art Review—Evolutionary Algorithms for Vehicle Routing. INFORMS Journal on Computing, 2009. [citation][year=2009]Z Ursani, D Essam, D Cornforth, R Stocker, Introducing the localized genetic algorithm for small scale capacitated vehicle routing problems. INFOR: Information Systems and Operational Research, 2009. [citation][year=2009]— J. Mendoza, A. Medaglia, and N. Velasco. An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems, 46(3):730–742, 2009; [citation][year=2009]**Potvin, Jean-Yves. A review of bio-inspired algorithms for vehicle routing. Springer Berlin Heidelberg, 2009. [citation][year=2008] A. J. Pohl and G. B. Lamont. Multi-objective uav mission planning using evolutionary computa- tion. In S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, and J. W. Fowler, editors, Winter Simulation Conference, pages 1268–1279. WSC, 2008; [citation][year=2008] J.-Y. Potvin. A review of bio-inspired algorithms for vehicle routing. Technical Report CIRRELT- 2008-30, Inter university Research Centre on Enterprise Networks, Logistics and Transportation, July 2008; [citation][year=2008] N. Suthikarnnarunai. A sweep algorithm for the mix fleet vehicle routing problem. In Proce- edings of the International MultiConference of Engineers and Computer Scientists, volume IIIMECS 2008, Hong Kong, March 2008; [citation][year=2008] G. Lamont. Uav swarm mission planning development using evolutionary algorithms - part ii. NATO-RTO SCI-195 Lecture Series, 2008. University of Strathclyde, Glasgow; [citation][year=2008] J.-Y. Potvin. A review of bio-inspired algorithms for vehicle routing. In F. B. Pereira and J. Tavares, editors, Bio-inspired Algorithms for the Vehicle Routing Problem, volume 161 of Studies in Computational Intelligence, pages 1–34. Springer, 2008; [citation][year=2007]Jean-Yves Potvin, Evolutionary Algorithms for Vehicle Routing, Technical Report CIRRELT-2007-48, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), November, 2007. [citation][year=2006] A. R. Alexander Grakovski and A. Medvedev. Optimisation of operational routing for sup- ply chain on the basis of genetic algorithms. In Proceedings of the 6th International Conference Reliability and Statistics in Transportation and Communication, 2006; [citation][year=2006] N. B. Fabien Tricoire and P. Guez. Applications d’algorithmes d’optimisation pour la determi- nation de la politique d’organisation des tournees de service. In 6e Conference Francophone de MOdelisation et SIMulation - MOSIM’06, 2006; [citation][year=2006] A. R. Alexander Grakovski and A. Medvedev. Vehicle routing problem for city services solution by hybrid genetic algorithm. In Proceedings of the 6th International Conference Reliability and Statistics in Transportation and Communication, 2006; [citation][year=2006] A. G. Daiva Griškeviciené. Sustainability of vilnius public transport system by the integration of all modes of passenger conveyance. In Proceedings of the 6th International Conference Reliability and Statistics in Transportation and Communication, 2006; [citation][year=2006] J. N. Slear. Afit uav swarm mission planning and simulation system. Master’s thesis, Depart- ment of the Air Force, Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, June 2006; [citation][year=2005] C.-H. Tseng. Theory and implementation of an intelligent vehicle dispatching system. Master’s thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2005; [citation][year=2005] M. A. Russell and G. B. Lamont. A genetic algorithm for unmanned aerial vehicle routing. Proceedings of the 2005 conference on Genetic and evolutionary computation GECCO 05, page 1523, 2005; [citation][year=2005] J. Schönberger. Operational Freight Carrier Planning: Basic Concepts, Optimization Models and Advanced Memetic Algorithms. Springer, 2005; [citation][year=2005] M. Russel. A genetic algorithm for uav routing integrated with a parallel swarm simulation. Master’s thesis, Department of the Air Force Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, 2005; [citation][year=2005] A. G. Qureshi. Analysis of the Effects of Cooperative Delivery System in Bangkok. PhD thesis, SCE : School of Civil Engineering, Asian Institute of Technology, Klong Luang, Thailand, 2005; [citation][year=2004]Keng Hoo Chuah, "Optimization and Simulation of Just in Time Supply and Delivery Systems", PhD Thesis, College of Engineering at the University of Kentucky, January 2004. [citation][year=2003]Wei-Che Chuang, An Inheritable Heuristic Algorithm for Bi-criteria Vehicle Routing Optimization Problems with Time Windows, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2003. [citation][year=2002]A. Tighe, and F. Smith, A Review of Artificial Intelligence Techniques in Fleet Logistics, Technical Report NUIG-IT-091002, Department of Information Technology, National University of Ireland, Galway, Ireland, 2002. [publication]Simões, A. and Costa, E. , "Using Genetic Algorithms to Deal with Dynamic Environments: A Comparative Study of Several Approaches Based on Promoting Diversity", 2002 [citation][year=2016]Ünal, A. N., & Kayakutlu, G. (2016). A Partheno-Genetic Algorithm for Dynamic 0-1 Multidimensional Knapsack Problem. RAIRO-Operations Research, 50(1), 47-66. [citation][year=2015]Heininger, K. (2015). Duality of stochasticity and natural selection: a cybernetic evolution theory. [citation][year=2013]S. M. J. Amali, S. Baskar (2013). Fuzzy logic-based diversity-controlled self-adaptive differential evolution. Engineering Optimization, Vol 45, 8, pp 1-17, Engineering Optimization, Taylor & Francis, 2013. [citation][year=2013]Ünal, A. N. (2013). A genetic algorithm for the multiple knapsack problem in dynamic environment. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2). [citation][year=2012]Demet Ayvaz, Haluk Rahmi Topcuoglu and Fikret Gurgen (2012). Performance evaluation of evolutionary heuristics in dynamic environments. Applied Intelligence, Volume 37, Number 1 (2012), pp. 130-144, Springer 2012. [citation][year=2012]André de Oliveira Gomes, Maury Meirelles Gouvêa Jr. (2012). Arquitetura Orientada a Serviços como Base para Implementação de um Algoritmo Evolucionário Paralelo, 2012. [citation][year=2011]Demet Ayvaz, Haluk Rahmi Topcuoglu and Fikret Gurgen (2011). Performance evaluation of evolutionary heuristics in dynamic environments. Applied Intelligence, pp 1-15, Springer Netherlands, 2011. [citation][year=2011]Shi Xu-hua, Qian Feng (2011). Artificial immune network multi-agent optimization strategy for dynamic environment. Control Theory and Applications, Vol 28, N0 7, pp. 921-930, 2011. [citation][year=2010]Z. Zhang and S. Qian, Immune algorithm with dynamic environments and its application to greenhouse control, in Journal of optimization and Engineering, Volume 11, Nº 1, pp. 125-144, 2010. [citation][year=2009]I. Dempsey, M. O´Neill, A. Brabazon (2009), Foundations in Grammatical Evolution for Dynamic Environments, Studies in Computational Intelligence, Vol. 194 , Springer-Verlag, 2009. [citation][year=2009]P. Rohlfshagen, X. Yao (2009), ?The Dynamic Knapsack Problem Revisited?. In M. Giacobini et al. (Eds.): EvoWorkshops 2009, Applications of Evolutionary Computing, LNCS 5484, pp. 745"754, Springer Verlag, 2009. [citation][year=2009]Adnan Acan, Ahmet Unveren (2009). "A memory-based colonization scheme for particle swarm optimization," IEEE Congress on Evolutionary Computation, pp. 1965-1972, IEEE 2009. [citation][year=2009]Z. Zhang, S. Qian (2009). Immune algorithm with antibody"repaired and its application for high"dimensional 0/ 1 knapsack problem. ApplicationResearch ofComputer, number 8, vol 26, pp. 2921 - 2939. [citation][year=2008]Zhuhong Zhang (2008), "Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse controlâ?. Applied Soft Computing, Volume 8, Issue 2, pp. 959-971, Elsevier, 2008. [citation][year=2008]Zhuhong Zhang, Shuqu Qian (2008). "Immune algorithm with dynamic environments and its application to greenhouse controlâ?. Journal Optimization and Engineering, ISSN 1389-4420, Publisher Springer Netherlands, 2008. [citation][year=2008]Chun-an Liu (2008). "New Dynamic Constrained Optimization PSO Algorithmâ?. Fourth International Conference on Natural Computation, Vol. 7, pp. 650-653, IEEE 2008. [citation][year=2008]Gang Zhao, Wenjuan Luo, Huiping Nie, Chen Li (2008). A Genetic Algorithm Balancing Exploration and Exploitation for the Travelling Salesman Problem. Proceedings of the Fourth International Conference on Natural Computation (ICNC 2008), pp. 505-509, IEEE 2008. [citation][year=2007]Iason Hatzakis (2007). "Multi-Objective Evolutionary Optimization in Time-Changing Environments?. PhD Thesis, MIT, 2007. [citation][year=2006]S. K. Basu, A. K. Bhatia (2006), "A naive genetic approach for non-stationary constrained problems?, In Soft Computing - A Fusion of Foundations, Methodologies and Applications), Volume 10, Number 2, pp. 152 - 162, January 2006, Springer. [citation][year=2006]K. Davoian (2006), Search Space Extension and PGAs: A Comparative Study of parallelization Schemes to Genetic Algorithms, Proceedings of the 24th IASTED International Multi-Conference on Artificial Intelligence and Applications, Austria, 13-16 February, 2006. [citation][year=2006]Robert Ian Bowers, Emre Sevinç (2006), "Preserving Variability in Sexual Multi-agent Systems with Diploidy and Dominance?, Engineering Societies in the Agents World VI, pp. 184-202, LNCS 3963, Springer, 2006 [citation][year=2005]Adnan Acan, "An External Partial Permutations Memory for Ant Colony Optimization". In Proceedings of the 5th European Conference, EvoCOP 2005, pp. 1--11, Lausanne, Switzerland, March/April, 2005. [citation][year=2005]Adnan Acan, Akin Gunäi (2005), "An External Memory Supported ACO for the Frequency Assinment Problem?, In B. Ribeiro et al (eds), Proceedings of the Seventh International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA"2005), pp. 365-368, Coimbra, Portugal, April 2005, Springer. [citation][year=2005]Adnan Acan, Akin Gunay (2005), "Enhanced Particle Swarm Optimization Through External Memory Support?, Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005) IEEE Press, 2005. [citation][year=2005]Adnan Acan, Yüce Tekol (2005). Performance-based Computation of Chromosome Lifetimes in Genetic Algorithms. In Knowlodge Incorporation in Evolutionary Computation, Y. Jin (Editor), pp. 195-213, Studies in Fuzziness and Soft Computing, Springer, 2005. [citation][year=2003]Johnny Kelsey, Jon Timmis, Andrew Hone (2003), "Chasing Chaos?, Congress on Evolutionary Computation, 2003. CEC '03, Volume 1, pp. 413" 419, IEEE 2003. [citation][year=2003]B. T. Luke (2003). Genetic Algorithms and Beyond. In Nature-Inspired Methods in Chemometrics, Riccardo Leardi (Editor), pp. 3-48, Data Handling in Science and Technology, Elsevier, 2003. [publication]Simões, A. and Costa, E. , "Parametric Study to Enhance the Genetic Algorithm's Performance when Using Transformation", 2002 [citation][year=2015]Gouvêa Jr, M. M., & Araújo, A. F. (2015). Evolutionary Algorithm with Diversity-Reference Adaptive Control in Dynamic Environments. International Journal on Artificial Intelligence Tools, 24(01), 1450013. [citation][year=2011]Terki Amel (2011). Analyse des performances des algorithmes génétiques utilisant différentes techniques d’évolution de la population. Mémoire Présenté pour obtenir le diplôme de Magister En Electronique, Université Mentouri Constantine, 2011. [citation][year=2008]Borenstein Yossi (2008), "Problem Hardness for Randomized Search Heuristics with Comparison-Based Selection: A Focus on Evolutionary Algorithms?. PhD Thesis, Department of Computer Science, University of Essex, June 2008. [citation][year=2007]B. Dilimulati, I. Bruha (2007). Genetic Algorithms in a dynamically changing environment. Data Mining VIII: Data, Text and Web Mining and their Business Applications. Transaction: Information and Communication Technologies volume 38, WitPress, 2007. [citation][year=2005]Borenstein Yossi, Ricardo Poli (2005), "Information Landscapes and the Analysis of Search Algorithms?, In H. "G Beyer et al (eds), Proceedings of the 2005 Genetic and Evolutionary Computation Conference (GECCO"2005), pp 1287-1294, Washington DC, USA, 25-29 June, ACM Press, 2005. [citation][year=2005]Borenstein Yossi, Ricardo Poli (2005), "Information and Performance Landscapes?, Technical Report CSM-440, ISSN: 1744-8050, University of Essex, 2005. [citation][year=2005]Pei-Chann Chang, , Shih-Hsin Chen and Kun-Lin Lin (2005), "Two-phase sub population genetic algorithm for parallel machine-scheduling problem?, Expert Systems with Applications , Volume 29, Issue 3 , October 2005, Pages 705-712, 2005. [citation][year=2003]Carsten Wilks, Thomas Schieder, and Rolf Eckmiller, Towards a Tactile Communication System with Dialog-based Tuning, International Joint conference on Neural Networks, IJCNN-2003, 2003, Portland, Oregon, USA. IJCNN2003. Piscataway, NJ, USA: IEEE, 2003. [citation][year=2003]B. T. Luke (2003). Genetic Algorithms and Beyond. In Nature-Inspired Methods in Chemometrics, Riccardo Leardi (Editor), pp. 3-48, Data Handling in Science and Technology, Elsevier, 2003. [publication]Pereira, F.B. and Tavares, J. and Penousal Machado and Costa, E. , "GVR: a New Genetic Representation for the Vehicle Routing Problem", 2002 [citation][year=2015]A survey of genetic algorithms for solving multi depot vehicle routing problem S Karakati?, V Podgorelec - Applied Soft Computing, 2015 - Elsevier [citation][year=2015]Route Optimization Method for Unmanned Air Vehicle Launched from a Carrier H Savuran, M Karakaya - Heron, 2015 - lnse.org [citation][year=2015]Genetic Algorithm Approach for a Class of Multi-Criteria, Multi-Vehicle Planner of UAVs E Freitas, JRH Carvalho - Evolutionary Multi-Criterion Optimization, 2015 - Springer [citation][year=2015]Efficient route planning for an unmanned air vehicle deployed on a moving carrier H Savuran, M Karakaya - Soft Computing, 2015 - Springer [citation][year=2015]Integrated Solutions for Delivery Planning and Scheduling in Distribution Centres G Merkuryeva, V Bolshakov - Applied Simulation and Optimization, 2015 - Springer [citation][year=2015]Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem L Feng, YS Ong, C Chen, X Chen - Soft Computing, 2015 - Springer [citation][year=2015]Simulation-based fitness landscape analysis and optimisation of complex problems G Merkuryeva, V Bolshakov - Technological and Economic …, 2015 - Taylor & Francis [citation][year=2015]Bidirectional Constructive Crossover for Evolutionary Approach to Travelling Salesman Problem S Kang, SS Kim, JH Won… - IT Convergence and [citation][year=2014]Multi-Objective Vehicle Routing Problems with Time Windows: A Vector Evaluated Artificial Bee Colony Approach OE Nahum, Y Hadas, U Spiegel - Int. J. Comput. Inf. Technol., 2014 [citation][year=2014]Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems. E Osaba, R Carballedo, F Diaz, E Onieva… - The Scientific World …, 2014 - [citation][year=2014]APPLICATION OF GENETIC ALGORITHMS TO VEHICLE ROUTING PROBLEM D Mocková, A Rybicková - Neural Network World, 2014 - search.proquest.com [citation][year=2014]The Real-Time Multi-Objective Vehicle Routing Problem-Information Availability and the Quality of the Results OE Nahuma, Y Hadasa, U Spiegela, R Cohenc [citation][year=2014]Crossover vs. Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems. E Osaba, R Carballedo, F Diaz, E Onieva [citation][year=2014]Integrated planning and scheduling built on cluster analysis and simulation optimisation G Merkuryeva, V Bolshakov - International Journal of …, 2014 - inderscienceonline.com [citation][year=2014]Multi-Objective Stochastic VRP–Fitness Calculation and Algorithm Converges Using a Generic Genetic Algorithm. OE NahumA, Y HadasA, U SpiegelA, R CohenC [citation][year=2014]A Two-Phase Scheduling Method Combined to the Tabu Search for the DARP A Lemouari, O Guemri - International Journal of Applied [citation][year=2013]Nahuma, Oren E., et al. "The Real-Time Multi-Objective Vehicle Routing Problem–Case Study: Information Availability and the Quality of the Results." [citation][year=2013]**Sand, Bastian. Parallele Algorithmen zur Lo?sung des Capacitated-Vehicle-Routing-Problems: Evaluierung des Einsatzes von Grafikkarten. Diss. Kaiserslautern, Technische Universita?t Kaiserslautern, Diss., 2013, 2013. [citation][year=2013]**Nahum, Oren E., Yuval Hadas, and Uriel Spiegel. "Multi-Objective Vehicle Routing Problems with Time Windows: a Vector Evaluated Artificial Bee Colony Approach." Transportation Research Board 92nd Annual Meeting. No. 13-0106. 2013. [citation][year=2013]**Nahuma, Oren E., et al. "The Real-Time Multi-Objective Vehicle Routing Problem–Case Study: Comparison of Three Algorithms." (2013). [citation][year=2013]**Nahum, Oren. The Real-Time Multi-Objective Vehicle Routing Problem. Diss. Ph. D.), Bar-Ilan University, Ramat-Gan, Israel, 2013. [citation][year=2012]X Chen, YS Ong. A Conceptual Modeling of Meme Complexes in Stochastic Search. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012. [citation][year=2012]Stefan Vonolfen, Andreas Beham, Michael Affenzeller, Stefan Wagner, Andreas Mayr. Combination and comparison of different genetic encodings for the vehicle routing problem. Computer Aided Systems Theory – EUROCAST 2011, LNCS, 2012 - Springer [citation][year=2012]K Pulji?. An evolutionary algorithm based on repeated mutations for solving the capacitated vehicle routing problem. Journal of Computing and Information Technology, 2012. [citation][year=2011]Xianshun Chen; Yew-Soon Ong; Meng-Hiot Lim; Kay Chen Tan; , "A Multi-Facet Survey on Memetic Computation," Evolutionary Computation, IEEE Transactions on , vol.15, no.5, pp.591-607, Oct. 2011 doi: 10.1109/TEVC.2011.2132725 [citation][year=2011]X. S. Chen, Y. S. Ong, M. H. Lim and S. P. Yeo. "Cooperating memes for vehicle routing problems", International Journal of Innovative Computing, Information and Control, vol. 7, no. 11, pp. 6483 – 6506, 2011. [citation][year=2011]Ziauddin Ursani, Daryl Essam, David Cornforth, Robert Stocker, Localized genetic algorithm for vehicle routing problem with time windows, Applied Soft Computing, Volume 11, Issue 8, December 2011, Pages 5375-5390, ISSN 1568-4946 [citation][year=2011]Vonolfen, S.; Affenzeller, M.; Beham, A.; Wagner, S.; , "Solving large-scale vehicle routing problem instances using an island-model offspring selection genetic algorithm," Logistics and Industrial Informatics (LINDI), 2011 3rd IEEE International Symposium on , vol., no., pp.27-31, 25-27 Aug. 2011 [citation][year=2011]Kim, Kyoung Cheol, "Vehicle routing and scheduling with delivery and installation", Oregon State University, PhD Thesis, May 2011 [citation][year=2011]Giovanni Giardini and Tamás Kalmár-Nagy, “Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem,” Mathematical Problems in Engineering, vol. 2011, Article ID 483643, 31 pages, 2011. doi:10.1155/2011/483643 [citation][year=2011]J SIROKY, V CEMPIREK, M SLIVONE. Software for building of delivery/pick-up vehicle routes. International Institute of Informatics and Systemics, 2011. [citation][year=2011]Q Ruan, Q Ruo, K Woghiren, L Miao. A hybrid genetic algorithm for the vehicle routing problem with three-dimensional loading constraints. Transportation Research Information Services , 2011. [citation][year=2010]RC Giles. A simulation study of cane transport system improvements in the Sezela Mill area. MSc. Thesis, 2010. [citation][year=2010]N Mukai, K Kawamura. Simulation evaluation for on-demand bus system with electrical vehicles. Intelligent Decision Technologies, 2010. [citation][year=2010]J Euchi, H Chabchoub. Adaptive Memory Procedure to solve the Profitable Arc Tour Problem. International Journal of Combinatorial Optimization Problems and Informatics, 2010. [citation][year=2010]— J. E. Mendoza, B. Castanier, C. Guéret, A. L. Medaglia, and N. Velasco. A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Computers & OR, 37(11):1886–1898, 2010; [citation][year=2010]— J. E. Mendoza, B. Castanier, C. Guéret, A. L. Medaglia, and N. Velasco. A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Computers & Operations Research, 37(11):1886–1898, 2010; [citation][year=2010]— N. Mukai and T. Watanabe. Route optimisation using evolutionary approaches for ondemand pickup problem. Int. J. Adv. Intell. Paradigms, 2:19–32, November 2010; [citation][year=2010]— A. K. L. Bouhafs, A. Hajjam. A hybrid heuristic approach to solve capacitated vehicle routing problem. Journal of Artificial Intelligence: theory and applications, 1(1):31–34, 2010 [citation][year=2009]Mendoza, Jorge E., Castanier, Bruno, Guéret, Christelle, Medaglia, Andrés L., and Velasco, Nubia, \textbf{A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands}, In Computers and Operations Research, June, 2009. [citation][year=2009]JY Potvin. A review of bio-inspired algorithms for vehicle routing. Bio-inspired Algorithms for the Vehicle Routing Problem. 2009 - Springer. [citation][year=2009]P Gwozdz, E Szlachcic. An adaptive selection evolutionary algorithm for the capacitated vehicle routing problem. Logistics and Industrial Informatics, LINDI 2009. [citation][year=2009]Z Ursani, D Essam, D Cornforth, R Stocker. Introducing the localized genetic algorithm for small scale capacitated vehicle routing problems. INFOR: Information Systems, 2009. [citation][year=2009]T Onoyama, T Maekawa, S Kubota, Setsuo Tsuruta, Norihisa Komoda. Solution of the vehicle routing problem for a cooperative logistics network by using multistage GA. Electrical Engineering in Japan, 2009. [citation][year=2009]— A. Garcia-Najera and J. A. Bullinaria. Bi-objective optimization for the vehicle routing problem with time windows: Using route similarity to enhance performance. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, EMO, volume 5467 of Lecture Notes in Computer Science, pages 275–289. Springer, 2009; [citation][year=2009]— F. de Bakker. Phoenix : Non-cooperative bargaining agents. Master’s thesis, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, The Netherlands, 2009; [citation][year=2009]— J.-Y. Potvin. State-of-the art review - evolutionary algorithms for vehicle routing. INFORMS Journal on Computing, 21(4):518–548, 2009; [citation][year=2009]— M. Affenzeller, S. Wagner, S. Winkler, and A. Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. CRC Press, 2009; [citation][year=2009]— J. Mendoza, A. Medaglia, and N. Velasco. An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems, 46(3):730–742, 2009; [citation][year=2009]— T. Giardini G., Kalmar-Nagy. Genetic algorithm for combinatorial planning: The subtour problem, 2009; [citation][year=2009]— J. E. Mendoza, B. Castanier, C. Guéret, A. L. Medaglia, and N. Velasco. A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Technical report, Centro para la Optimización y Probabilidad Aplicada (COPA), 1, March 2009; [citation][year=2008]E Alba, B Dorronsoro . Cellular genetic algorithms. Springer, 2008. [citation][year=2008]John T. Langton, Joseph A. Carol, Brad Rosenberg. An Evolutionary Algorithm Technique for Intelligence, Surveillance, and Reconnaissance Plan Optimization. Evolutionary and Bio-Inspired Computation: Theory and Applications II, 2008. [citation][year=2008]Liu Yao, Cheng Guoquan, Wang Zhuan, Hu Haiqin, Liu Kui. Improved Genetic Algorithm for Variable Fleet Vehicle Routing Problem with Soft Time Window. 6th IEEE International Conference on Industrial Informatics (INDIN 2008), 2008. [citation][year=2007]— Takashi Onoyama, Takuya Maekawa, Kubota, Setsuo and Hisashi Tsuruta: "The delivery programming technique in the joint physical distribution net by multiple stage GA?, electric study theory C, Vol. 127, No. 9, pp.1460-1467 (2007). [citation][year=2007]— H. Lim. A genetic algorithm for the vehicle routing problem with heterogeneous vehicles from multiple depots, allowing multiple visits. Master’s thesis, Industrial Engineering, Oregon State University, 2007; [citation][year=2007]— K. S. Takashi Onoyama, Takuya Maekawa and H. Tsuruta. Vehicle routing problem solving method for a cooperative logistics network by using multi-stage ga. IEEJ Transactions on Electronics, Information and Systems, 127(9):1460–1467, 2007; [citation][year=2007]— J.-Y. Potvin. Evolutionary algorithms for vehicle routing. Technical Report CIRRELT-2007- 48, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, November 2007; [citation][year=2007]— B. Dorronsoro, D. Arias, F. Luna, A. Nebro, and E. Alba. A grid-based hybrid cellular genetic algorithm for very large scale instances of the cvrp. In High Performance Computing & Simulation Conference, Prague, Czech Republic, June 2007; [citation][year=2007]— A. Treitz. Online Vehicle Routing Probleme im Krankenhaus. GRIN Verlag, 2007; [citation][year=2007]— B. Dorronsoro, A. Nebro, D. Arias, and E. Alba. Un Algoritmo Genético Híbrido Paralelo para Instancias Complejas del ProblemaVRP. In F. A. et al., editor, Actas del Quinto Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB’07), pages 135–141, Puerto de la Cruz, Tenerife, España, 2007; [citation][year=2007]— J. E. Mendoza, B. Castanier, C. Guéret, A. L. Medaglia, and N. Velasco. An evolutionary based decision support system for vehicle routing: the case of a public utility. Technical report, Departamento de Ingeniería Industrial, Universidad de los Andes, 2007; [citation][year=2006]\item Onoyama, T., Maekawa, T., Komoda, N., \textbf{GA Applied VRP Solving method for a Cooperative Logistics Network}, Emerging Technologies and Factory Automation, 2006. ETFA '06. IEEE Conference on, pp.1101-1106, 20-22 Sept. 2006. [citation][year=2006]\item Guillermo Gonz·lez Vargas, and Felipe Gonz·lez Aristiz·bal, \textbf{Metaheuristics applied to vehicle routing. A case study. Parte 1: formulating the problem}, Revista Ingenierÿa e Investigaciÿn, vol.26 no.3, Bogot·, December 2006. [citation][year=2006]\item R. M. Jorgensen, J. Larsen, and K. B. Bergvinsdottir, \textbf{Solving the Dial-a-Ride problem using genetic algorithms}, Journal of the Operational Research Society, advance online publication, 13 September 2006. [citation][year=2006]\item James N. Slear, \textbf{AFIT UAV Swarm Mission Planning and Simulation System}, PhD Thesis, Department of Electrical and Computer Engineering, Graduate School of Engineering and Management, Air Force Institute of Technology, Air University, Wright-Patterson Air Force Base, Ohio USA, June, 2006. [citation][year=2006]\item Oliver Kunze, \textbf{Tourenplanung f¸r den eCommerce-Lebensmittel-Heimlieferservice}, PhD Thesis, Fakultaten Maschinenbau, Universitat Karlsruhe, Germany, March, 2006. [citation][year=2005]Ali Gul Qureshi, "Analysis of the Effects of Cooperative Delivery System in Bangkok", Thesis, AIT Thesis no.TE-04-10, SCE : School of Civil Engineering, Asian Institute of Technology, Klong Luang, Thailand, 2005. [citation][year=2005]Ryota Itai (Kansai University Graduate School), Tadahiko Murata (Kansai University)"A Two-Fold EMO Algorithm for Three-Objective Vehicle Routing Problem Considering Delivering Seasons?, Institute of Electrical evolution technical investigation ad hoc committee 2nd Conference "evolution technology and information system? (Kyoto, September 22,23rd, 2005). [citation][year=2005]Medaglia, A. L., and Gutiérrez, E. JGA: An Object-Oriented Framework for Rapid Development of Genetic Algorithms. In Handbook of Research on Nature Inspired Computing for Economy and Management. Jean-Phillipe Rennard (Ed.). 2005. [citation][year=2005]G.G. Mitchell, Validity Constraints and the TSP GeneRepair of Genetic Algorithms, In Proceedings of The IASTED International Conference on Artificial Intelligence and Applications (AIA 2005), as part of the 23rd IASTED International Multi-Conference on APPLIED INFORMATICS, Innsbruck, Austria, February 14-16, 2005. [citation][year=2005]M. Russel, and G. Lamont A Genetic Algorithm for Unmanned Aerial Vehicle Routing, In Proceedings of the Genetic and Evolutionary Computation Conference, Washinghton D.C., USA, 25-29 June, 2005. [citation][year=2005]M. Russel, A Genetic Algorithm for UAV Routing Integrated with a parallel Swarm Simulation, Master Thesis, Department of the Air Force Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, May, 2005. [citation][year=2005]Ribeiro, G. M. and Lorena, L. A. N. Roteamento de veiculos dinamicos usando algoritmos geneticos XIX ANPET - Congresso de Pesquisa e Ensino em Transportes - Recife /PE - 7 a 11 de novembro de 2005 [citation][year=2005]Jörn Schönberger, \textbf{Operational Freight Carrier Planning: Basic Concepts, Optimization Models and Advanced Memetic Algorithms}, Published by Springer, ISBN 3540253181, 9783540253181, 2005. [citation][year=2005]Claus Friedrich, "The Periodical Vehicle Routing Problem: Research Overview and Practical Application to a South German Fast Food Restaurant", Diploma Thesis, University of Augsburg, 2005. [citation][year=2005]Cheng-Hung Tseng, Theory and Implementation of an Intelligent Vehicle Dispatching System, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2005. [citation][year=2004]Philip James Uren, Investigation of Distributed and Parallel Performance of a Genetic Algorithm, B.Sc. Thesis, School of Computing, University of Tasmania, November, 2004. [citation][year=2004]A. Mendez, M. Pontin, M. Ziletti, M. Carnero, and J. Hernández, "RECOLECCIÿN DE RESIDUOS PATÿGENOS. UN ENFOQUE EVOLUTIVO HÍBRIDO", Mecanica Computacional Vol. XXIII, G.Buscaglia, E.Dari, O.Zamonsky (Eds.), Bariloche, Argentina, November 2004 [citation][year=2004]Alfredo Olivera, "Heur?sticas para Problemas de Ruteo de Veh?culo", Technical Report TR0408, Instituto de Computacion, Facultad de Ingenier?a, Universidad de la Republica, Montevideo, Uruguay, August, 2004. [citation][year=2004]Wangzu Pillar, Cheng Ka-hing, Fang Hong, and Qian Fu Lan, An Hybrid Optimization Algorithm Solving Vehicle Routing Problems, Operations Research and Management Science, Vol.13 No.6 P.48-52, 2004. [citation][year=2004]K. B. Bergvinsdottir, The Genetic Algorithm for solving the Diala-Ride Problem, Master Thesis, Department of Informatics and Mathematical Modelling, Technical University of Denmark, May, 2004. [citation][year=2004]A. S. Bjarnadottir, Solving the Vehicle Routing Problem with Genetic Algorithms, Master Thesis, Department of Informatics and Mathematical Modelling, Technical University of Denmark, April, 2004. [citation][year=2004]M. Chen, X.G. Jian, F.H. Sun, Y.P. Ma, and Z.M. Zhang, Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows, In Advances in Materials Manufacturing Science and Technology, Materials Science Forum, vol. 471-472, pp. 801-805, 2004. [citation][year=2004]A. Agarwal, M. Lim, M. Y. Kyaw, and M. J. Er, Inflight Rerouting for an Unmanned Aerial Vehicle, In Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, USA, 26-30 June, 2004. [citation][year=2004]A. Agarwal, M. Lim, C. Y. Chew, T. K. Poo, M. J. Er, and Y. K. Leong, Solution to the Fixed Airbase Problem for Autonomous URAV Site Visitations Sequencing, In Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, USA, 26-30 June, 2004. [citation][year=2004]R. J. Carmo, Uma Analise da Eficiencia dos Algoritmos Geneticos no Roteamento de Ve?culos, Bachelor Thesis, Universidade do Estado da Bahia, Brasil, Maio, 2004. [citation][year=2003]Andreas Treitz, Online Vehicle Routing Probleme im Krankenhaus, Diplomarbeit, Universität des Saarlandes, 2003. [citation][year=2003]Wei-Che Chuang, An Inheritable Heuristic Algorithm for Bi-criteria Vehicle Routing Optimization Problems with Time Windows, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2003. [citation][year=2003]G. G. Mitchell, D. O'Donoghue, D. Barnes, and M. McCarville, GeneRepair - A Repair Operator for Genetic Algorithms, late-breaking paper, GECCO, Chicago USA, July 2003. [citation][year=2003]K. Q. Zhu, A Diversity-controlling Adaptive Genetic Algorithm for the Vehicle Routing Problem with Time Windows, In 15th IEEE International Conference on Tools for Artificial Intelligence (ICTAI 2003), Sacramento, California USA, 3-5 November, 2003. [citation][year=2003]J. Lysgaard, A.N. Letchford, and R.W. Eglese, A New Branch-and-cut Algorithm for Capacitated Vehicle Routing Problems, In Mathematical Programming, Springer-Verlag, 2003. [citation][year=2002]— A. Tighe and F. Smith. A review of artificial intelligence techniques in fleet logistics. Technical report, National University of Ireland, 2002; [publication]Penousal Machado and Tavares, J. and Pereira, F.B. and Costa, E. , "Vehicle Routing Problem: Doing it the Evolutionary Way", in Genetic and Evolutionary Computation Conference, GECCO-02, 2002 [citation][year=2015]Genetic Algorithm Approach for a Class of Multi-Criteria, Multi-Vehicle Planner of UAVs E Freitas, JRH Carvalho - Evolutionary Multi-Criterion Optimization, 2015 - Springer [citation][year=2015]Simpler is Better: a Novel Genetic Algorithm to Induce Compact Multi-label Chain Classifiers EC Gonçalves, A Plastino, AA Freitas - … of the 2015 on Genetic and …, 2015 - dl.acm.org [citation][year=2013]**Manríquez Manríquez, Carlos Ernesto, and Cristián Oliva San Martín. "Resolución de un caso particular de diseño de rutas para vehículos mediante la programación lineal entera." (2013). [citation][year=2013]**Babu, Shoban, and Mitul Shah. "Meta Heuristic Approach for Automatic Forecasting Model Selection." International Journal of Information Systems and Supply Chain Management (IJISSCM) 6.2 (2013): 1-16. [citation][year=2012]A Soeanu, S Ray, M Debbabi, J Berger, A. Boukhtouta. A Learning Based Evolutionary Algorithm For Distributed Multi-Depot VRP. Advances in Knowledge-Based and Intelligent Information and Engineering Systems, IOS Press, 2012. [citation][year=2012]M Ibrahimov, A Mohais, S Schellenberg. EVOLUTIONARY APPROACHES FOR SUPPLY CHAIN OPTIMISATION. PART I: SINGLE AND TWO-COMPONENT SUPPLY CHAINS. Journal of Intelligent Systems, 2012. [citation][year=2012]X Li . Evolutionary mechanism design using agent-based models. PhD Thesis, 2012. [citation][year=2012]J Wojtusiak, T Warden, O Herzog. The learnable evolution model in agent-based delivery optimization. Memetic Computing, 2012 - Springer. [citation][year=2012]**Adamidis, Panagiotis, Christos Voliotis, and Evaggelia Pliatsika. "An Evolutionary Algorithm for a Real Vehicle Routing Problem." [citation][year=2012]**Monteiro, Marta Sofia Rodrigues. "Ant Colony Optimization Algorithms to solve Nonlinear Network Flow Problems." [citation][year=2011]Ibrahimov, M.; Mohais, A.; Schellenberg, S.; Michalewicz, Z.; Comparison of different evolutionary algorithms for global supply chain optimisation and parameter analysis, Evolutionary Computation (CEC), 2011 IEEE Congress on, pp. 2407 - 2414, 5-8 June 2011 [citation][year=2010]Maksud Ibrahimov, Neal Wagner, Arvind Mohais, Sven Schellenberg, Zbigniew Michalewicz (2010). Comparison of cooperative and classical evolutionary algorithms for global supply chain optimisation. WCCI 2010 IEEE World Congress on Computational Intelligence - IEEE CEC, pp. 1602-1609, July, 18-23, 2010 - CCIB, Barcelona, Spain, IEEE, 2010. [citation][year=2010]T Kirt, J Kivimaa. Optimizing it security costs by evolutionary algorithms. Conference on Cyber Conflict Proceedings, 2010. [citation][year=2009]W Jiang, Y Zhang, J Xie. A particle swarm optimization algorithm with crossover for vehicle routing problem with time windows. IEEE Symposium on Computational Intelligence in Scheduling, 2009. [citation][year=2009]T Weise, A Podlich, C Gorldt. Solving real-world vehicle routing problems with evolutionary algorithms. Natural Intelligence for Scheduling, Planning and Packing Problems Studies in Computational Intelligence, 2009 - Springer. [citation][year=2009]Z Ursani, D Essam, D Cornforth, R Stocker. Introducing the localized genetic algorithm for small scale capacitated vehicle routing problems. INFOR: Information Systems and Operational Research, 2009. [citation][year=2009]N Suthikarnnarunai, E Olinick. Improving Transportation Services for the University of the Thai Chamber of Commerce: A Case Study on Solving the Mixed-Fleet Vehicle Routing Problem with Split Deliveries. AIP Conference Proceedings, International MultiConference of Engineers and Computer Scientists, 2009 [citation][year=2009]J.-Y. Potvin. State-of-the art review - evolutionary algorithms for vehicle routing. INFORMS Journal on Computing, 21(4):518–548, 2009; [citation][year=2009]S. C. Oimoen. Dynamic network formation using ant colony optimization. Master’s thesis, Department of the Air Force, Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, 2009; [citation][year=2009]F. L. J. Woods. A cost assessment of the dayton public schools vehicle routing problem. Master’s thesis, Department of the Air Force, Air University, Air Force Institute of Technology, Wright- Patterson Air Force Base, 2009; [citation][year=2008]Adam J. Pohl, \textbf{Multi-Objective UAV Mission Planning Using Evolutionary Computation}, Master Thesis, Department of the Air Force, Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, March, 2008. [citation][year=2008]N. Suthikarnnarunai, \textbf{A Sweep Algorithm for the Mix Fleet Vehicle Routing Problem}, Proceedings of the International MultiConference of Engineers and Computer Scientists 2008 Vol IIIMECS 2008, 19-21 March, 2008, Hong Kong. [citation][year=2008]Wuxin Huang Shili, and Tan Xianhua Li, \textbf{Inverse Kinematics of Compliant Manipulator Based on the Immune Genetic Algorithm}, In Proceddings of the Fourth International Conference on Natural Computation(ICNC '08), Volume: 4, On page(s): 390-394, Date: 18-20 Oct. 2008. [citation][year=2008]Xiaozhe Yang , \textbf{Vehicle Routing Problem with Time Windows and Driving/Working Time Restrictions}, Master Thesis, Faculty of the Russ College of Engineering and Technology of Ohio University, November 2008. [citation][year=2007]L. Polat, A. Acan, and A. Ünveren. Cooperative coevolutionary algorithms for fuzzy vehicular routing problem: An analysis of efficiency vs. geographical distribution. In IEEE Congress on Evolutionary Computation, pages 1126–1132. IEEE, 2007; [citation][year=2007]R. Drezewski, L. Dronka, and J. Kozlak. Co-operative co-evolutionary system for solving dynamic vrptw problems with crisis situations. In V. Mavrík, V. Vyatkin, and A. W. Colombo, editors, HoloMAS, volume 4659 of Lecture Notes in Computer Science, pages 81–92. Springer, 2007; [citation][year=2007]A. F. Güneri. Physical distribution activities and vehicle routing problems in logistics ma- nagement: a case study. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 221(1):123–133, 2007; [citation][year=2007]J.-Y. Potvin. Evolutionary algorithms for vehicle routing. Technical Report CIRRELT-2007- 48, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, November 2007; [citation][year=2007]G. Giardini and T. Kalmár-Nagy. Performance metrics and evaluation of a path planner based on genetic algorithms. In Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems, PerMIS ’07, pages 84–90, New York, NY, USA, 2007. ACM; [citation][year=2007]J. M. S. Grau. Criteris de localització de terminals de consolidació en empreses de paqueteria. PhD thesis, Departament d’Infraestructura del Transport i del Territori, Universitat Politécnica de Catalunya, 2007; [citation][year=2007]G. G. Mitchell. Evolutionary Computation Applied to Combinatorial Optimisation Problems. PhD thesis, School of Electronic Engineering, Dublin City University, 2007; [citation][year=2006]Jia Ma; Hao Zou; Li-Qun Gao; Dan Li, \textbf{Immune Genetic Algorithm for Vehicle Routing Problem with Time Windows}, in Proceedings of the IEEE 2006 International Conference on Machine Learning and Cybernetics, Volume , Issue , 13-16 Aug. 2006 Page(s):3465 - 3469 [citation][year=2006]Hans Ole Rafaelsen, Frank Eliassen and Sharath Babu Musunoori, Towards Self-organizing Distribution Structures for Streaming Media, In Proceedings of On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE, OTM Confederated International Conferences, CoopIS, DOA, GADA, and ODBASE 2006, 1825-1842, Montpellier, France, October 29 - November 3, 2006. [citation][year=2006]Guillermo González Vargas, and Felipe González Aristizábal, "Metaheuristics applied to vehicle routing. A case study. Parte 1: formulating the problem", Revista Ingeniería e Investigación, vol.26 no.3, Bogotá, December 2006. [citation][year=2006]Mathias Kern, "Parameter Adaptation in Heuristic Search: A Population-Based Approach", PhD Thesis, Department of Computer Science, University of Essex, 2006. [citation][year=2005]Jörn Schönberger, Operational Freight Carrier Planning: Basic Concepts, Optimization Models and Advanced Memetic Algorithms, Published by Springer, ISBN 3540253181, 9783540253181, 2005. [citation][year=2005]Cheng-Hung Tseng, Theory and Implementation of an Intelligent Vehicle Dispatching System, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2005. [citation][year=2005]N. Armstrong, and K. Mock, Helicopter Routing for Maintaining Remote Sites in Alaska using a Genetic Algorithm, In Proceedings of 20th National Conference on Artificial Intelligence (AAAI 2005), Pittsburgh, USA, July 9-13, 2005. [citation][year=2005]M. Russel, and G. Lamont A Genetic Algorithm for Unmanned Aerial Vehicle Routing, In Proceedings of the Genetic and Evolutionary Computation Conference, Washinghton D.C., USA, 25-29 June, 2005. [citation][year=2005]M. Russel, A Genetic Algorithm for UAV Routing Integrated with a parallel Swarm Simulation, Master Thesis, Department of the Air Force Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, May, 2005. [citation][year=2004]Philip James Uren, Investigation of Distributed and Parallel Performance of a Genetic Algorithm, B.Sc. Thesis, School of Computing, University of Tasmania, November, 2004. [citation][year=2004]Wangzu Pillar, Cheng Ka-hing, Fang Hong, and Qian Fu Lan, An Hybrid Optimization Algorithm Solving Vehicle Routing Problems, Operations Research and Management Science, Vol.13 No.6 P.48-52, 2004. [citation][year=2004]S. Tsutsui, and G. Wilson, Solving Capacitated Vehicle Routing Problems Using Edge Histogram Based Sampling Algorithms, In Proceedings of the 2004 Congress on Evolutionary Computation (CEC"04), pp. 1150-1157, Portland, USA, 20-23 June, 2004. [citation][year=2004]R. J. Carmo, Uma Analise da Eficiencia dos Algoritmos Geneticos no Roteamento de Ve?culos, Bachelor Thesis, Universidade do Estado da Bahia, Brasil, Maio, 2004. [citation][year=2003]Chih-Hua Lee, \textbf{Genetic Algorithms for Post-Earthquake Road-Network Emergency Repairing Scheduling Problem}, Master Thesis, Transportation & Communication Management Science, Feng Chia University, Taiwan, August 2003. [citation][year=2003]Wei-Che Chuang, An Inheritable Heuristic Algorithm for Bi-criteria Vehicle Routing Optimization Problems with Time Windows, Master's Thesis, Graduate Institute of Information Engineering, Feng Chia University, Taiwan, 2003. [citation][year=2003]J. Berger, and M. Barkaoui, A new hybrid genetic algorithm for the capacitated vehicle routing problem, In Journal of the Operational Research Society, vol. 54, no. 12, pp. 1254-1262, December, 2003. [citation][year=2003]A. Freitag, Strategien der Tourenplanung unter besonderer Berucksichtigung der Entsorgung von Risikomaterialien, Master Thesis, Chemnitz Technical University, Germany, December, 2003. [citation][year=2003]J. Berger, and M. Barkaoui, A Hybrid Genetic Algorithm for the Capacitated Vehicle Routing Problem, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 646-656, Chicago, USA, 12-16 July, 2003. [publication]Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "An Empirical Comparison of Particle Swarm and Predator Prey Optimisation", in AICS 2002, vol. 2464, 2002 [citation][year=2010]Quande Qin, Rongjun Li, Ben Niu and Li Li, A new PSO model mimicking bio-parasitic behavior, in Advances in Swarm Intelligence, LNCS Vol. 6145, pp. 68-77, Springer, 2010. [citation][year=2010]S Chowdhury, GS Dulikravich (2010). Improvements to single-objective constrained predator"prey evolutionary optimization algorithm. Structural and Multidisciplinary Optimization, Vol 41, Number 4m pp. 541-554, Springer, 2010 [citation][year=2009]Cheng-Jian Lin, Cheng-Hung Chen and Chin-Teng Lin (2009). A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications IEEE Transactions on Systems, Man, and Cybernetics"Part C: Applications and Reviews, 39(1), pp. 55-68, IEEE 2009. [citation][year=2009]Kusum Deep A and Jagdish Chand Bansal A (2009). Mean particle swarm optimisation for function optimisation. International Journal of Computational Intelligence Studies, Volume 1, Number 1, pp. 72-92, InderScience Publishers. [citation][year=2009]Marco A. Montes de Oca, Jorge Peña, Thomas Stützle, Carlo Pinciroli, and Marco Dorigo (2009). Heterogeneous Particle Swarm Optimizers. IRIDIA " Technical Report Series, Technical Report No. TR/IRIDIA/2009-001, January 2009. [citation][year=2009]S Chowdhury, GS Dulikravich, RJ Moral (2009). Modified predator-prey algorithm for constrained and unconstrained multi-objective optimisation. International Journal of Mathematical Modelling and Numerical Optimisation, Volume 1, Number 1-2, pp. 1 - 38, 2009. [citation][year=2009]Shuyuan Wu and Anthony Brabazon (2009). The Emergence of a Market: What Efforts Can Entrepreneurs Make? Natural Computing in Computational Finance, Studies in Computational Intelligence, Vol 185, pp. 225-243, Springer 2009. [citation][year=2009]Souma Chowdhury and George S. Dulikravich (2009). Improvements to single-objective constrained predator"prey evolutionary optimization algorithm. Structural and Multidisciplinary Optimization, pp. 1-14, Springer 2009. [citation][year=2009]Zhou Xian-cheng (2009). Image Segmentation Based on Modified Particle Swarm Optimization and Fuzzy C-Means Clustering. Second International Conference on Intelligent Computation Technology and Automation, vol. 1, pp.611-616, 2009 [citation][year=2009]Muhammad Rashid, Abdul Rauf Baig, Kashif Zafar, "Niching with Sub-swarm Based Particle Swarm Optimization," icctd, vol. 2, pp.181-183, 2009 International Conference on Computer Technology and Development, 2009 [citation][year=2009]G. H. Shakouri, K. Shojaee, H. Zahedi (2009).An effective particle swarm optimization algorithm embedded in SA to solve the traveling salesman problem. Proceedings of the 21st annual international conference on Chinese control and decision conference, pp. 5581-5586, IEEE Press, 2009. [citation][year=2009]I. Dempsey, M. O'Neill, A. Brabazon (2009), "Foundations in Grammatical Evolution for Dynamic Environments?, Studies in Computational Intelligence, Vol. 194 , Springer-Verlag, 2009. [citation][year=2008]Shu-Yuan Wu A and Anthony K. Brabazon A (2008). A garbage can model for Schumpeterian process: the network effects. International Journal of Foresight and Innovation Policy, Volume 4, Number 3-4, pp. 287-106, InderScience Publishers. [citation][year=2008]Mitsuharu Higashitani, Atsushi Ishigame, Keiichiro Yasuda (2008). Pursuit-Escape Particle Swarm Optimization. IEEJ Transactions on Electrical and Electronic Engineering, Volume 3 Issue 1, Pages 136 - 142, Wiley 2008. [citation][year=2008]Xian-cheng Zhou, Qun-tai Shen, Li-mei Liu (2008). New two-dimensional fuzzy C-means clustering algorithm for image segmentation. Journal of Central South University of Technology, Volume 15, Number 6, Springer 2008. [citation][year=2008]Lin, CJ; Chen, CH; Lin, CT. Efficient self-evolving evolutionary learning for neuro-fuzzy inference systems. IEEE Transactions on Fuzzy Systems, 16 (6): 1476-1490, December, 2008. [citation][year=2008]Liu,Y; Qin, Z Eleite astrategy for particcle swarm optimization algorithms, Proceedings of the International Conference on Information Coomputing and Automation,vols. 1-3:673-677, 2008. [citation][year=2007]Yu Liu, Zheng Qin, Zhewen Shi, Jiang Lu. Center particle swarm optimization, Neurocomputing, Volume 70 , Issue 4-6 (January 2007), pp.672-679, ISSN:0925-2312, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, The Netherlands, 2007. [citation][year=2007]Sébastien Piccand , Michael O'Neill and Jacqueline Walker (2007). Scalability of particle swarm Algorithms. Proceedings of the 9th annual conference on Genetic and evolutionary computation, Ant colony optimization, swarm intelligence, and artificial immune systems: posters, pp. 179 " 179, ACM Press, 2007. [citation][year=2007]Alec Banks , Jonathan Vincent and Chukwudi Anyakoha, A review of particle swarm optimization. Part I: background and development , Journal Natural Computing , Publisher Springer Netherlands, 2007. [citation][year=2007]Ho, S. L.; Yang, S. Y.; Ni, G. Z.; Wong, K. F., An Improved PSO Method With Application to Multimodal Functions of Inverse Problems, IEEE Transactions on Magnetics, Volume 43, Issue 4, pp 1597 " 1600, April 2007. [citation][year=2007]Fang Gao , Qiang Zhao , Hongwei Liu and Gang Cui (2007): Cultural Particle Swarm Algorithms for Constrained Multi-objective Optimization. Book Computational Science " ICCS 2007, Lecture Notes in Computer Science, Volume 4490, pp. 1021-1028, Springer, 2007. [citation][year=2007]Z Wu, D Jiang, M Wei, Y L (2007): Dynamical Evolution in Function Finding. In Natural Computation, 2007, vol 5, pp. 614-618, IEEE Press. [citation][year=2007]Mitsuharu Higashitani, Atsushi Ishigame, Keiichiro Yasuda, Pursuit-Escape Particle Swarm Optimization, IEEJ Transactions on Electrical and Electronic Engineering, Volume 3, Number 1, pp. 136-142. [citation][year=2006]Brabazon, Anthony; O'Neill, Michael, Biologically Inspired Algorithms for Financial Modelling, Springer Verlag, 2006. [citation][year=2006]Michael O"Neill, Anthony Brabazon, Grammatical Swarm: The generation of programs by social programming, Natural Computing, , Volume 5, Issue 4, pp. 443-462, 2006. [citation][year=2006]Gao, F. Liu, H. Zhao, Q. Cui, G., Virus-Evolutionary Particle Swarm Optimization Algorithm , Lecture Notes in Computer Science, 2006, NUMB 4222, pages 156-165, Springer-Verlag. [citation][year=2006]Qin, Z. Yu, F. Shi, Z. Wang, Y., Adaptive Inertia Weight Particle Swarm Optimization, Lecture Notes in Computer Science, 2006, Vol. 4029, pages 450-459, Springer-Verlag. [citation][year=2006]Ho, S.L. Yang, S. Ni, G. Wong, H.C., A Particle Swarm Optimization Method With Enhanced Global Search Ability for Design Optimizations of Electromagnetic Devices, IEEE Transactions on Magnetics, April 2006, Volume: 42, pp. 1107- 1110 [citation][year=2005]Ho,S.L, Yang, S. Ni, G., Lo, E. Wong, H., A particle swarm optimization-based method for multiobjective design optimizations, IEEE Transactions on Magnetics, Vol. 41, Nº 5, May 2005 [citation][year=2005]Andries P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley, 2005. [citation][year=2005]Salima Nabti, Souham Meshoul, and Mohamed Batouche, Predator Prey Optimizer for Unsupervised Clustering in Image Segmentation, International Arab Conference on Information Technology, ACIT'2005, December 6th- 8th, 2005, Al-Isra Private University, Jordan. [citation][year=2005]Volker Strunk, Räuber-Beute-Mechanismen zur Lenkung von Populationen in Evolutionären Algorithmen, Diplomarbeit, Universität Dortmund, Fachbereich Informatik, April 2005. [citation][year=2005]Janson and Martin Middendorf, A Hierarchical Particle Swarm Optimizer and Its Adaptive Variant Stefan, IEEE Transactions On Systems, Man, And Cybernetics"Part B: Cybernetics, Vol. 35, No. 6, December 2005. [citation][year=2004]Grammatical Swarm, Michael O�Neill , and Anthony Brabazon , Proceedings of GECCO-2004,Seattle,USA , LNCS 3102, p. 163 ff. [citation][year=2004] Supervisor-Student Model in Particle Swarm Optimization, Yu Liu , Zheng Qin , Xingshi He, in Proceedings of the Congress on Evolutionary Computation (CEC 2004), pp 542-547 [citation][year=2004]The Automatic Generation of Programs for Classification Problems with Grammatical Swarm, Michael O�Neill, Anthony Brabazon, Catherine Adley, in Proceedings of the Congress on Evolutionary Computation (CEC 2004) � pp104-110. [citation][year=2004] Franken N. (2004) PSO-based coevolutionary game learning. MSc thesis, Department of Computer Science, University of Pretoria, South Africa [citation][year=2004]Pontus Svenson, Christian M�rtenson, Hedvig Sidenbladh, Michael Malm, Swarm Intelligence for logistics: Background, FOI-R--1180�SE, February 2004, ISSN 1650-1942, Swedish Defense, Research Agency. [publication]Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "Chasing the Swarm: A Predator-Prey Approach to Function Optimisation", in Mendel 2002, 2002 [citation][year=2009]M. Kathrada (2009). The flexi-PSO: Towards a more flexible particle swarm optimizer. OPSEARCH, Volume 46, Number 1, pp. 52-68, Springer 2009. [citation][year=2006]Cecília Di Chio, Extended particle swarm to simulate biology-like systems. Proceedings of the 1rst European Graduate Workshop on Evolutionary Computation (EvoPhD 2006), M. Giacobini and J. Van Hemert (Eds.), Budapest, Hungary, 10-12 April, 2006. [citation][year=2005]Salima Nabti, Souham Meshoul, and Mohamed Batouche, Predator Prey Optimizer for Unsupervised Clustering in Image Segmentation, International Arab Conference on Information Technology, ACIT'2005, December 6th- 8th, 2005, Al-Isra Private University, Jordan. 2001(6 publications) [publication]Simões, A. and Costa, E. , "On Biologically Inspired Genetic Operators: Transformation in the Standard Genetic Algorithm", 2001 [citation][year=2012]Ali Karci and Ahmet Arslan (2012). Uniform Population In Genetic Algorithms. Journal of Electrical & Electronics, Vol. 2, Num 2, pp 495-504, 2012. [citation][year=2012]Lili Liu, Dingwei Wang and Jiafu Tang (2012). An immune system based differential evolution algorithm using near-neighbor effect in dynamic environments. Journal of Control Theory and Applications, 10(4), pp 417-425, Springer, 2012. [citation][year=2011]Zaheed Ahmed and Irfan Younas (2011). A Dynamic Programming based GA for 0-1 Modified Knapsack Problem. International Journal of Computer Applications 16(7):1–6, Foundation of Computer Science, 2011. [citation][year=2011]M. C. Goldbarg, P. H. Asconavieta da Silva, E. F. G. Goldbarg (2011). Algoritmos Evolucionários na Solução do Problema do Caixeiro Alugador. Lopes and Takahashi (Eds). Computação Evolucionária em Problemas de Engenharia, Capítulo 14, pp. 301-330, ISBN 978-85-94619-00-5, 2011. [citation][year=2011]Wang Gui-ping, Zhang Shuai (2011). Solution for Magic Square Problem Based on Bidirectional Breadth-first Search. Journal on Computer Engineering, Vol 37, N0 20, pp. 219-222, October 2011. [citation][year=2011]Maury Meirelles Gouvêa Jr., Aluizio Fausto Ribeiro Araújo (2011). Diversity-Based Adaptive Evolutionary Algorithms. New Achievements in Evolutionary Computation, Numerical Analysis and Scientific Computing, November 2011. [citation][year=2009]E. Goldbarg, M. Goldbarg (2009). Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms. Foundations of Computational Intelligence, Volume 3, Global Optimization, Studies in Computational Intelligence, pp. 425-460, Springer 2009 [citation][year=2008]Carlos Perales-Graván, Rafael Lahoz-Beltra (2008): "An AM Radio Receiver Designed With a Genetic Algorithm Based on a Bacterial Conjugation Genetic Operator?. IEEE Transactions on Evolutionary Computation, Vol. 12, 2, pp. 129-142, IEEE Press, April 2008. [citation][year=2008]M. C. Goldbarg, L. B. Bagi, and E. Goldbarg (2008). "Algoritmo transgenético aplicado ao problema do caixeiro comprador capacitado simétrico?, Pesquisa Operacional, Jan./Apr. 2008, vol. 28, n.1, pp 93-121, ISSN 0101-7438, Scientific Electronic Library Online, 2008. [citation][year=2008]Shengxiang Yang (2008). "Genetic Algorithms with Memory and Elitism-Based Immigrants in Dynamic Environments?. Evolutionary Computation, Vol. 16, No. 3, pp. 385-416, MIT Press, Fall 2008. [citation][year=2008]Shengxiang Yang, X. Yao (2008). "Population-based incremental learning with associative memory for dynamic environments?. IEEE Transactions on Evolutionary Computation, 12(5): 542-561, IEEE Press, October 2008. [citation][year=2008]Carlos Perales Graván (2008). P.E.T.R.I. (Programming Evolution Through Reiterated Infection): Diseño de un Algoritmo Genético inspirado en Mecanismos Genéticos Microbianos. Phd Thesis, Facultad de Ciencias Biológicas de la Universidad Complutense de Madrid, ISBN: 978-84-669-3309-4, Madrid 2008. [citation][year=2007]Elizabeth F. G. Goldbarg, Marco C. Goldbarg, Ligia B. Bagi (2007), "Transgenetic Algorithm: a New Evolutionary Perspective for Heuristics Design?, Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, London, United Kingdom. WORKSHOP SESSION: Evolution of natural and artificial systems - metaphors and analogies in single and multi-objective, pp. 2701-2708, ISBN:978-1-59593-698-1, ACM Press, 2007. [citation][year=2007]B. Dilimulati, I. Bruha (2007). Genetic Algorithms in a dynamically changing environment. Data Mining VIII: Data, Text and Web Mining and their Business Applications. Transaction: Information and Communication Technologies volume 38, WitPress, 2007. [citation][year=2006]Shengxiang Yang (2006), "A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments?, Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (GECCO 2006), pp 1377"1384, Seattle, Washington, USA, 2006. [citation][year=2005]Laurent Bonnasse-Gahot, "Using genetic algorithms to evolve locomotion in artificial creatures", ÿcole Nationale Supérieure des Télécommunications, Paris, ISSN-0751-1345, 2005. [citation][year=2003]B. T. Luke (2003). Genetic Algorithms and Beyond. In Nature-Inspired Methods in Chemometrics, Riccardo Leardi (Editor), pp. 3-48, Data Handling in Science and Technology, Elsevier, 2003. [citation][year=2002]Alam Shah Jamal (2002), "Teacher"s Evaluation Using Genetic Algorithms?, Proceedings of the 2002 IEEE International Conference on System, Man and Cybernetics, Hammamet, Tunisia, 6-9 October, IEEE Press, 2002. [publication]Simões, A. and Costa, E. , "Using Biological Inspiration to Deal with Dynamic Environments", 2001 [citation][year=2015]Shabash, B., & Wiese, K. C. (2015). Diploidy in evolutionary algorithms for dynamic optimization problems-a best-chromosome-wins dominance mechanism. International Journal of Intelligent Computing and Cybernetics. [citation][year=2012]Lauren Davis, Funda Samanlioglu, Xiaochun Jiang, Daniel Mota, Paul Stanfield (2012). A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP). In Journal of Computers and Operations Research, Volume 39 Issue 1, January, Elsevier Science, 2012. [citation][year=2011]Ruben Ruiz-Torrubiano, Alberto Suarez (2011). The TransRAR crossover operator for genetic algorithms with set encoding. Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO 2011), pp. 489-496, Dublin, Ireland, ACM Press, 2011. [citation][year=2011]S. Siva Sathya, S. Kuppuswami (2011). Gene silencing—A genetic operator for constrained optimization. Applied Soft Computing, Volume 11, Issue 8, pp. 5801–5808, Elsevier 2011. [citation][year=2011]Walid Tfaili (2011). Continuous Dynamic Optimization, Ant Colony Optimization - Methods and Applications, Avi Ostfeld (Ed.), ISBN: 978-953-307-157-2, InTech, DOI: 10.5772/14794, 2011. [citation][year=2010]Alvaro Gomes, C. Henggeler Antunes, A. Gomes Martins (2010). Improving the responsiveness of NSGA-II using an adaptive mutation operator: a case study. International Journal of Advanced Intelligence Paradigms, Volume 2, Number 1, pp. 4-18, InderScience Publishers, 2010. [citation][year=2009]P. Rohlfshagen, X. Yao (2009), ?The Dynamic Knapsack Problem Revisited?. In M. Giacobini et al. (Eds.): EvoWorkshops 2009, Applications of Evolutionary Computing, LNCS 5484, pp. 745"754, Springer Verlag, 2009. [citation][year=2009]Adnan Acan, Ahmet Unveren (2009). "A memory-based colonization scheme for particle swarm optimization," IEEE Congress on Evolutionary Computation, pp. 1965-1972, IEEE 2009. [citation][year=2008]Walid Tfaili and Patrick Siarry (2008), "A new charged ant colony algorithm for continuous dynamic optimization?. Journal of Applied Mathematics and Computation, Vol. 197, Issue 2, pp. 604-613, Elsevier, April 2008. [citation][year=2007]Walid Tfaili, Johann Dréo and Patrick Siarry (2007), "Fitting of an Ant Colony approach to Dynamic Optimization through a new set of test functions?. International Journal of Computational Intelligence Research, vol. 3, pp. 205-218, 2007. [citation][year=2007]Adnan Acan, Ahmet Unveren (2007), "A shared-memory ACO+GA hybrid for combinatorial optimization?. IEEE Congress on Evolutionary Computation, 2007. CEC 2007, pp. 2078-2085, IEEE 2007. [citation][year=2005]Adnan Acan, An External Partial Permutations Memory for Ant Colony Optimization. In Proceedings of the 5th European Conference, EvoCOP 2005, pp. 1--11, Lausanne, Switzerland, March/April, 2005, Lecture Notes in Computer Science, Vol. 3448, Springer Berlin. [citation][year=2005]Adnan Acan, Akin Gunay (2005), Enhanced Particle Swarm Optimization Through External Memory Support, Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005) IEEE Press, 2005. [citation][year=2005]Simon X. Yang , Jin Zeng, Guoyin Wang (2005), "Modelling Of Supercritical Fluid Extraction Using Dynamic Genetic Algorithm Based Optimisation?. In Proceedings of 16th IFAC World Congress, Prague, 2005. [citation][year=2005]Adnan Acan, Yüce Tekol (2005). Performance-based Computation of Chromosome Lifetimes in Genetic Algorithms. In Knowlodge Incorporation in Evolutionary Computation, Y. Jin (Editor), pp. 195-213, Studies in Fuzziness and Soft Computing, Springer, 2005. [citation][year=2004]Adnan Acan (2004), "An External Memory Implementation in Ant Colony Optimization". In Proceedings of the 4th International Workshop ANTS 2004: Ant Colony, Optimization and Swarm Intelligence, Brussels, Belgium, September 5-8, 2004. Lecture Notes in Computer Science, Vol. 3172, Springer, Berlin. [citation][year=2003]Jarmo T. Alander (2003), Indexed bibliography of genetic algorithms in the the Mediterranean Countries. Report Series No. 94-1- MEDITER, Department of Electrical Engineering and Production Economics, University of Vaasa, Finland, 2003. [citation][year=2003]Jarmo T. Alander (2003), Indexed bibliography of genetic algorithms in the Latin America, Portugal and Spain. Report Series No. 94-1-LATIN, Department of Electrical Engineering and Production Economics, University of Vaasa, Finland, 2003. [publication]Simões, A. and Costa, E. , "An Evolutionary Approach to the Zero/One Knapsack Problem: Testing Ideas from Biology", 2001 [citation][year=2014]Spirov, A. V., Zagriychuk, E. A., & Holloway, D. M. (2014). Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites. Parallel Processing Letters, 24(02). [citation][year=2014]Palanisamy, V. (2014). Enhanced Technique To Improve The Performance of Genetic Algorithm. Australian Journal of Basic and Applied Sciences, 8(17), 447-453. [citation][year=2012]Lauren Davis, Funda Samanlioglu, Xiaochun Jiang, Daniel Mota, Paul Stanfield (2012). A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP). In Journal of Computers and Operations Research, Volume 39 Issue 1, January, Elsevier Science, 2012. [citation][year=2012]Jiri Jaros and Petr Pospichal (2012). A Fair Comparison of Modern CPUs and GPUs Running the Genetic Algorithm under the Knapsack Benchmark. Applications of Evolutionary Computation, Lecture Notes in Computer Science, 2012, Volume 7248, 426-435, Springer 2012. [citation][year=2011]Zaheed Ahmed and Irfan Younas (2011). A Dynamic Programming based GA for 0-1 Modified Knapsack Problem. International Journal of Computer Applications 16(7):1–6, Foundation of Computer Science, 2011. [citation][year=2011]Ruben Ruiz-Torrubiano, Alberto Suarez (2011). The TransRAR crossover operator for genetic algorithms with set encoding. Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO 2011), pp. 489-496, Dublin, Ireland, ACM Press, 2011. [citation][year=2011]S. Siva Sathya, S. Kuppuswami (2011). Gene silencing—A genetic operator for constrained optimization. Applied Soft Computing, Volume 11, Issue 8, pp. 5801–5808, Elsevier 2011. [citation][year=2011]S. Siva Sathya, S. Kuppuswami, S. Sendhil Kumar (2011). Gene Silencing Genetic Algorithm for 0/1 Knapsack with Object Preferences. International Journal of Computational Intelligence Systems, Volume 4, Issue 5, pp. 886-893, Taylor&Francis;, 2011. [citation][year=2010]Rubén Ruiz-Torrubiano, Sergio García-Moratilla and Alberto Suárez (2010). Optimization Problems with Cardinality Constraints. Computational Intelligence in Optimization Applications and Implementations, Vol 7, pp. 105-130, Springer Berlin Heidelberg, 2010. [citation][year=2010]A. Coelho (2010). Seleção de Portfólio no Contexto de Grades P2P Multi-serviço. PhD Thesis, Centro de Engenharia Elétrica e Informática, Universidade Federal de Campina Grande, 2010. [citation][year=2009]E. Goldbarg, M. Goldbarg (2009). Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms. Foundations of Computational Intelligence, Volume 3, Global Optimization, Studies in Computational Intelligence, pp. 425-460, Springer 2009. [citation][year=2009]Cristian Ruican (2009). Developing Automatic Synthesis Methodologies for Quantum Circuits using Genetic Algorithms. PhD Thesis, Teze de doctorat ale UPT, Seria 10, Nr. 14, Editura Politehnica, ISSN:1842-7707, 2009. [citation][year=2008]S. Ito, Y. Mitsukura, H. N. Miyamura, T. Saito and Minoru Fukumi (2008), "A Visualization of Genetic Algorithm Using the Pseudo-color?, Neural Information Processing, LNCS 4985, pp. 444-452, Springer, 2008. [citation][year=2008]M. C. Goldbarg, L. B. Bagi, and E. Goldbarg (2008). "Algoritmo transgenético aplicado ao problema do caixeiro comprador capacitado simétrico?, Pesquisa Operacional, Jan./Apr. 2008, vol. 28, n.1, pp 93-121, ISSN 0101-7438, Scientific Electronic Library Online, 2008. [citation][year=2008]Daniel Dombrowski (2008). "Evolutionäre Algorithmen für das Rucksackproblem?. MSc Thesis, Dortmund Technical University, 2008. [citation][year=2007]Nga Lam Law, K.Y. Szeto (2007): Adaptive Genetic Algorithm with Mutation and Crossover Matrices. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2330-2333, Hyderabad, India, January 2007. [citation][year=2007]Antonios Litke, Dimitrios Skoutas, Konstantinos Tserpes and Theodora Varvarigou (2007). "Efficient task replication and management for adaptive fault tolerance in mobile Grid environments?. Future Generation Computer Systems, ISSN:0167-739X, Volume 23 , Issue 2, pp. 163 " 178, 2007. [citation][year=2007]Elizabeth F. G. Goldbarg, Marco C. Goldbarg, Ligia B. Bagi (2007), "Transgenetic Algorithm: a New Evolutionary Perspective for Heuristics Design?, Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, London, United Kingdom. WORKSHOP SESSION: Evolution of natural and artificial systems - metaphors and analogies in single and multi-objective, pp. 2701-2708, ISBN:978-1-59593-698-1, ACM Press, 2007. [citation][year=2004]Chun Wai Ma, Kwok Yip Szeto (2004), "Locus Orientated Adaptive Genetic Algorithm: Application to the Zero/One Knapsack Problem". In A. Lofti (ed.), Proceedings of the Fifth International Conference On Recents Advances in Soft Computing, pp 410-415, Nottingham, United Kingdom, 16-18 December, 2004. [citation][year=2003]B. T. Luke (2003). Genetic Algorithms and Beyond. In Nature-Inspired Methods in Chemometrics, Riccardo Leardi (Editor), pp. 3-48, Data Handling in Science and Technology, Elsevier, 2003. [citation][year=2002]Alexander V.Spirov, Alexander V. Kazansky (2002), "Jumping Genes-Mutators Can Rise Efficacy of Evolutionary Search". In William B. Langdon et al. (eds), Proceedings of the 2002 Genetic and Evolutionary Computation Conference (GECCO"2002), pp 561-568, New York, USA, 9-13 July, San Francisco, CA: Morgan Kaufmann, 2002. [publication]Pereira, F.B. and Costa, E. , "Understanding the Role of Learning in the Evolution of Busy Beavers: a Comparison Between the Baldwin Effect and a Lamarckian Strategy", 2001 [citation][year=2012]S Whiteson. Evolutionary Computation for Reinforcement Learning. Reinforcement Learning, 2012 - Springer [citation][year=2010]Shimon A. Whiteson. Adaptive Representations for Reinforcement Learning. Studies in Computational Intelligence, Springer, 2010. [citation][year=2009]T Kovacs. Genetics-based machine learning. Handbook of Natural Computing: Theory, Experiments, 2009. [citation][year=2007]Shimon A. Whiteson (2007). Adaptive Representations for Reinforcement Learning, Ph.D. Thesis, The University of Texas at Austin. [citation][year=2006]S. Whiteson, P. Stone. Evolutionary Function Approximation for Reinforcement Learning. Journal of Machine Learning Research, Vol. 7, pp. 877-917 [citation][year=2006]D. Curran (2006). An Empirical Analysis of Cultural Learning: Examining Fitness, Diversity and Changing Environments in Populations of Game-Playing Network Agents. Ph.D. Thesis, National University of Ireland, Galway, Ireland. [citation][year=2002]COELHO, Leandro dos Santos; KROHLING, Renato A. "Discrete Variable Structure Control Design based on Lamarckian Evolution. In: 7TH ONLINE WORLD CONFERENCE ON SOFT COMPUTING IN INDUSTRIAL APPLICATIONS, 2002. Advances in Soft Computing: Engineering Design and Manufacturing. Springer-Verlag, 2002. v. 1, p. 361-370 [publication]Pereira, F.B. and Costa, E. , "The Influence of Learning in the Evolution of Busy Beavers", 2001 [citation][year=2006]Paszkovicz, W. Properties of a GA extended by a random self-learning operator and asymetric mutations: a convergence study for a task of powder-pattern indexing. Analytica Chimica Acta, 566 (1): 81-98 April 2006. [publication]Pereira, F.B. and Costa, E. , "How Learning Improves the Performance of Evolutionary Agents: a Case Study with an Information Retrieval System for a Distributed Environment", 2001 [citation][year=2007]D.Curran, C.O'Riordan, H.Sorensen An analysis of the effects of Lifetime learning on population fitness and diversity in an NK fitness landscape ECAL Lisbon, September, 2007. [citation][year=2007]D Curran, C O'Riordan. The effects of cultural learning in populations of neural networks. Artificial life, 2007 - MIT Press [citation][year=2006]D. Curran and C. O'Riordan, Increasing Population Diversity Through Cultural Learning, Adaptive Behavior, 14 (4). [citation][year=2006]D. Curran, C.O'Riordan Examining the Effects of Cultural Learning on Fitness and Diversity in Populations of Neural Networks in a Dynamic Environments 17th Irish Artificial Intelligence and Cognitive Science Conference (AICS 2006), 2006. [citation][year=2006]D. Curran and C. O'Riordan, Sequential Task Problem Solving using Cultural Learning in Populations of Neural Networks, Artificial Life, 13 (1). [citation][year=2006]D. Curran (2006). An Empirical Analysis of Cultural Learning: Examining Fitness, Diversity and Changing Environments in Populations of Game-Playing Network Agents. Ph.D. Thesis, National University of Ireland, Galway, Ireland. [citation][year=2004]D. Curran and C.O'Riordan , Evolving Agents to play connect-four using cultural learning Irish Artificial Intelligence and Cognitive Science Conference. [citation][year=2004]D.Curran, C.O'Riordan Evolving Connect-Four Playing Neural Networks Using Cultural Learning Technicl Report of Dept. of Information Retrieval, NUI, Galway. [citation][year=2003]D. Mirikitani, I. Kushchu (2003). E. Coli Search: Self Replicating Agents for Web Based Information Retrieval. In Proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL-2003), pp. 622-629, LNCS 2690, Springer. [citation][year=2002]Bauer, T. e Leake, D. (2002). Calvin: A Multi-Agent Personal Information Retrieval System. In Agent Oriented Information Systems 2002: Proceedings of the 4th International Bi-Conference Workshop (AOIS-2002), CEUR WS Proceedings. 2000(10 publications) [publication]Simões, A. and Costa, E. , "Using Genetic Algorithms with Sexual or Asexual Transposition: A Comparative Study", 2000 [citation][year=2015]Zamdborg, L., Holloway, D. M., Merelo, J. J., Levchenko, V. F., & Spirov, A. V. (2015). Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem. Information sciences, 306, 88-110. [citation][year=2014]Palanisamy, V. (2014). Enhanced Technique To Improve The Performance of Genetic Algorithm. Australian Journal of Basic and Applied Sciences, 8(17), 447-453. [citation][year=2013]A Tretyakova, F Seredynski (2013). A Novel Genetic Algorithm with Asexual Reproduction for the Maximum Lifetime Coverage Problem in Wireless Sensor Networks. INFOCOMP 2013, The Third International Conference on Advanced Communications and Computation, pp. 87-93, IARIA 2013. [citation][year=2011]Terki Amel (2011). Analyse des performances des algorithmes génétiques utilisant différentes techniques d’évolution de la population. Mémoire Présenté pour obtenir le diplôme de Magister En Electronique, Université Mentouri Constantine, 2011. [citation][year=2011]S. Siva Sathya, S. Kuppuswami (2011). Gene silencing—A genetic operator for constrained optimization. Applied Soft Computing, Volume 11, Issue 8, pp. 5801–5808, Elsevier 2011. [citation][year=2011]S. Siva Sathya, S. Kuppuswami, S. Sendhil Kumar (2011). Gene Silencing Genetic Algorithm for 0/1 Knapsack with Object Preferences. International Journal of Computational Intelligence Systems, Volume 4, Issue 5, pp. 886-893, Taylor&Francis;, 2011. [citation][year=2010]Tung-Kuan Liu, Chiu-Hung Chen and Jyh-Horng Chou (2010). "Optimization of short-haul aircraft schedule recovery problems using a hybrid multiobjective genetic algorithm?. Expert Systems with Applications, Volume 37, Issue 3, pp. 2307-2315, Elsevier 2010. [citation][year=2008]Dragan Radulovic (2008): Pure Random Search with exponential rate of convergence, Optimization 2008, 1-15, Taylor&Francis, 2008. [citation][year=2008]Ningchuan Xiao (2008). A Unified Conceptual Framework for Geographical Optimization Using Evolutionary Algorithms. Annals of the Association of American Geographers, Volume 98, Number 4, 2008 , pp. 795-817(23), Routledge, part of the Taylor & Francis Group, 2008. [citation][year=2007]Ningchuan Xiao, David A. Bennett, Marc P. Armstrong (2007): "Interactive Evolutionary Approaches to Multiobjective Spatial Decision Making: A Synthetic Review?, Computers, Environment and Urban Systems ,Volume 31, Issue 3, May 2007, Pages 232-252, Elsevier 2007. [citation][year=2007]Adnan Acan, Ahmet Unveren (2007), "A shared-memory ACO+GA hybrid for combinatorial optimization?. IEEE Congress on Evolutionary Computation, 2007. CEC 2007, pp. 2078-2085, IEEE 2007. [citation][year=2007]Ka Yui Tang (2007). "Enhancing Evolutionary Algorithms Using Transposition?. Bachelor of Science in Computer Information Systems with Honours, University of Bath, 2007. [citation][year=2006]Carlos A. Coello Coello (2006): List of References on Constraint-Handling Techniques used with Evolutionary Algorithms, CINVESTAV-IPN , Sección de Computación, Departamento de Ingeniería Eléctrica, 2006. [citation][year=2005]C. A. Coello Coello (2005), "List of References on Constraint-Handling Techniques used with Evolutionay Algorithms". Sección de Computación, Departamento de Ingeniaria Eléctrica, México, 2005. [citation][year=2004]Brian Beachkofsk (2004): "Confidence Interval Minimization Through Experimental Design? , PhD Dissertation, Wright State University, 2004. [citation][year=2004]Nguyen Van Hop, Mario T. Tabucanon (2004): "Improvement of Search Process in Genetic Algorithms: An Application of PCB Assembly Sequencing Problem.?. In Godfrey Onwubolu, B. Babu (editors): New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, volume 141, pp. 385-399, Springer. [citation][year=2003]M. J. Appel, R. LaBarre, D. Radulovic (2003), On Accelerated Random Search, Society for Industrial and Applied Mathematics Journal on Optimization, Volume 14, Number 3, pp. 708-731, 2003. [citation][year=2002]B. K. Beachkofski, G. B. Lamont (2002), "Evolutionary Programming Based Stratified Design Space Sampling?. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 193 " 200, Morgan Kaufmann Publishers, New York, 9-13 July, 2002. [citation][year=2002]David J. Caswell (2002). "Active Processor Scheduling Using Evolutionary Algorithms?. Master Thesis, Air Force Institute of Technology, Wright-Patterson AFB School of Engineering and Management, December 2002. [publication]Simões, A. and Costa, E. , "Using Genetic Algorithms with Asexual Transposition", 2000 [citation][year=2014]Spirov, A. V., Zagriychuk, E. A., & Holloway, D. M. (2014). Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites. Parallel Processing Letters, 24(02). [citation][year=2012]Tüze Kuyucu, Ivan Tanev, Katsunori Shimohara (2012). Incremental Evolution of Fast Moving and Sensing Simulated Snake-like Robot with Multiobjective GP and Strongly-typed Crossover. Memetic Computing special issue on Optimization of Complex Systems, Springer, 2012. [citation][year=2012]Marco Van Etten (2012). On the Profitability of Intraday Technical Trading Rules Using Genetic Programming. Estimetrics publication, 2012. [citation][year=2011]Zaheed Ahmed and Irfan Younas (2011). A Dynamic Programming based GA for 0-1 Modified Knapsack Problem. International Journal of Computer Applications 16(7):1–6, Foundation of Computer Science, 2011. [citation][year=2011]Tuze Kuyucu, Ivan Tanev, Katsunori Shimohara (2011). Genetic Transposition Inspired Incremental Genetic Programming for Efficient Coevolution of Locomotion and Sensing of Simulated Snake-like Robot. Advances in Artificial Life ECAL 2011 Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (2011). [citation][year=2010]Emmanuel Ofori Oppong (2010). Optimal resource allocation using knapsack problems a case study of television advertisements at Ghana television (GTV). Mater Thesis, College of Arts and Social Sciences, Kwame Nkrumah Univeristy of Science and Technology, 2010. [citation][year=2009]Alexander V. Spirov, Alexander B. Kazansky, Leonid Zamdborg, Juan J. Merelo and Vladimir F. Levchenko (2009). Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem. In arxiv.org, 2009. [citation][year=2008]Carlos Perales-Graván, Rafael Lahoz-Beltra (2008): "An AM Radio Receiver Designed With a Genetic Algorithm Based on a Bacterial Conjugation Genetic Operator?. IEEE Transactions on Evolutionary Computation, Vol. 12, 2, pp. 129-142, IEEE Press, April 2008. [citation][year=2008]Carlos Perales Graván (2008). P.E.T.R.I. (Programming Evolution Through Reiterated Infection): Diseño de un Algoritmo Genético inspirado en Mecanismos Genéticos Microbianos. Phd Thesis, Facultad de Ciencias Biológicas de la Universidad Complutense de Madrid, ISBN: 978-84-669-3309-4, Madrid 2008. [citation][year=2008]H. Sallam, C. S. Regazzoni, I.Talkhan and A. Atiya (2008). The Effect of Genetic Operations on the Diversity of Evolvable Neural Networks. IADIS International Conference Intelligent Systems and Agents 2008, pp. 143-150. [citation][year=2007]Ka Yui Tang (2007). "Enhancing Evolutionary Algorithms Using Transposition?. Bachelor of Science in Computer Information Systems with Honours, University of Bath, 2007. [citation][year=2006]Tomasz Dominic Gwiazda, Genetic Algorithms Reference (Volume I), TG Editor, ISBN 8392395832, 2006. [citation][year=2005]McGregor, S. Harvey, I., "Embracing plagiarism: theoretical, biological and empirical justiification for copy operators in genetic optimization", in Genetic Programming and Evolvable Hardware, 6 (4), pp. 407-420. [citation][year=2005]Khorsand, A.-R. Akbarzadeh-T, M.-R. (2005): "Quantum gate optimization in a meta-level genetic quantum algorithm?. In IEEE International Conference on Systems, Man and Cybernetics, Vol 4. pp. 3055-3062. [citation][year=2004]Nguyen Van Hop, Mario T. Tabucanon (2004): "Improvement of Search Process in Genetic Algorithms: An Application of PCB Assembly Sequencing Problem.?. In Godfrey Onwubolu, B. Babu (editors): New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, volume 141, pp. 385-399, Springer. [citation][year=2003]J. T. Alander (2003), Indexed Bibliography of Genetic Algorithm Theory and Comparisons. Report Series No. 94-1-THEORY, Department of Electrical Engineering and Production Economics, University of Vaasa, 2003. [citation][year=2003]Jarmo T. Alander (2003), Indexed bibliography of genetic algorithms in the the Mediterranean Countries. Report Series No. 94-1- MEDITER, Department of Electrical Engineering and Production Economics, University of Vaasa, Finland, 2003. [citation][year=2003]Jarmo T. Alander (2003), Indexed bibliography of genetic algorithms in the Latin America, Portugal and Spain. Report Series No. 94-1-LATIN, Department of Electrical Engineering and Production Economics, University of Vaasa, Finland, 2003. [citation][year=2001]Paul J. Kennedy, Thomas R. Osborn (2001), "A Double-Stranded Encoding Scheme with Inversion Operator for Genetic Algorithms". In Spector, L., E. Goodman, A. Wu, W.B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon, and E. Burke, editors. 2001 Proceedings the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 398-407, San Francisco, USA, 7-11 July, CA: Morgan Kaufmann Publishers, 2001. [publication]Pereira, F.B. and Costa, E. , "How Adaptive Agents Learn to Deal with Incomplete Queries in Distributed Environments", 2000 [citation][year=2013]BL Iantovics, CB Zamfirescu. ERMS: AN EVOLUTIONARY REORGANIZING MULTIAGENT SYSTEM. International Journal of Innovative Computing, Information and Control, 2013. [citation][year=2009]P. Cristea (2009). Application of Neural Networks in Image Processing and Visualization. In Geo-Spatial Visual Analytics: Geographic Information Processing and Visual Analytics for Environmental Security, R. Amicis et. at. (Eds.), pp. 59-71, Springer. [citation][year=2008]PD Cristea. Use of intelligent evolutionary agents in the analysis of genomic signals. Proceedings of the 10th WSEAS International Conference, 2008. [citation][year=2005]Kushchu, I. (2005). Web-Based Evolutionary and Adptive Information Retrieval, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 2, pp. 117-125 [citation][year=2003]D. Mirikitani, I. Kushchu (2003). E. Coli Search: Self Replicating Agents for Web Based Information Retrieval. In Proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL-2003), pp. 622-629, LNCS 2690, Springer. [citation][year=2000]Cristea, P. Arsene, A. e Nitulescu, B. (2000). Evolutionary Intelligent Agents. In Proceedings of the Conference on Evolutionary Computation, Special Session on Evolutionary Intelligent Agents (CEC-2000), pp. 1320-1328, San Diego, EUA [publication]Pereira, F.B. and Penousal Machado and Costa, E. and Amilcar Cardoso and Ochoa-Rodriguez, A. and Santana, R. and Soto, M. , "Too Busy to Learn", 2000 [citation][year=2015]Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem L Zamdborg, DM Holloway, JJ Merelo, VF Levchenko… - Information …, 2015 - Elsevier [citation][year=2011]Spirov, Alexander V., et al. "FORCED EVOLUTION IN SILICO BY ARTIFICIAL TRANSPOSONS AND THEIR GENETIC OPERATORS: THE ANT NAVIGATION PROBLEM." 2011. [citation][year=2009]C. Lima (2009). Substructural Local Search in Discrete Estimation of Distribution Algorithms. Ph.D. Thesis, Universidade do Algarve. [citation][year=2009]**Spirov, Alexander V., et al. "Forced evolution in silico by artificial transposons and their genetic operators: the john muir ant problem." arXiv preprint arXiv:0910.5542 (2009). [citation][year=2002]Nattee Niparnan, A Genetic Algorithm for Finite State Machine Inference, PhD Thesis, Chulalongkorn University, 2002. [publication]Santana, R. and Ochoa-Rodriguez, A. and Soto, M. and Pereira, F.B. and Penousal Machado and Costa, E. and Amilcar Cardoso , "Probabilistic Evolution and the Busy Beaver Problem", 2000 [citation][year=2010]**Santana, Roberto, Pedro Larrañaga, and José A. Lozano. "Learning factorizations in estimation of distribution algorithms using affinity propagation." Evolutionary Computation 18.4 (2010): 515-546. [citation][year=2007]Ramón Sagarna, Test Data Generation: Applications of Estimation of Distribution Algorithms and Scatter Search, PhD Thesis, University of the Basque Country. 2007. [citation][year=2005]Alexander, M. A., "Parallel implementation of Estimation of Distribution Algorithms based on probabilistic graphical models", PhD Thesis, University of País Vasco, 2005. [citation][year=2003]P Larranaga, JA Lozano, H Muhlenbein, "Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problemsâ?. nteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial. No.19 (2003), pp. 149-168 ISSN: 1137-360. [citation][year=2003]José A. Lozano, H. Mühlenbein, Pedro Larrañaga, Algoritmos de Estimación de Distribuciones en Problemas de Optimización Combinatoria, Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN 1137-3601, Vol. 7, Nº. 19, 2003 , págs. 149-168 [publication]Pereira, F.B. and Costa, E. , "The Influence of Learning in the Behavior of Information Retrieval Adaptive Agents", 2000 [citation][year=2013]BL Iantovics, CB Zamfirescu. ERMS: AN EVOLUTIONARY REORGANIZING MULTIAGENT SYSTEM. International Journal of Innovative Computing, Information and Control, 2013. [citation][year=2007]W.Ke, J. Mostafa, Y. Fu. "Collaborative Classifier Agents: Studying the Impact of Learning in Distributed Document Classification", In Proceedings of the 2007 Conference on Digital Libraries (JCDL'07), pp. 428-437, ACM. [citation][year=2003]F. Cuadrado, J. M. Molina (2003). Evolutionary society of dynamical agents. An application to software estimation. 10th ISPE International Conference on Concurrent Engineering: Research and Applications. pp 387-392. Madeira Island, Portugal, July 26"30, 2003. [citation][year=2003]F. Cuadrado, J. M. Molina (2003). Processing of Satellite Images by means of an Evolving Software Agent Society, "Workshop Aplicaciones de la Inteligencia Artificial en la Ingeniería? Conferencia de la Asociación Española de Inteligencia Artificial CAEPIA"2003, San Sebastian, Spain, November 2003. [citation][year=2002]F. Cuadrado, J. M. Molina (2002). Design and Evolution of a Dynamical Agent Society, Internet e-com and Artificial Intelligence. I International Workshop on Practical Applications of Agents and Multiagent Systems. pp. 13-24. Salamanca, Spain, October 23-25, 2002. [citation][year=2001]F. Cuadrado, J. M. Molina (2001). Evolving software agent societies interchanging functionalities, IEEE International Conference on Systems, Man and Cybernetics. pp. 823-828. USA. October 2001. [citation][year=2001]F. Cuadrado, J. M. Molina, J. Carbó (2001). Evolutionary Agent Community based on Dynamical Components?, Workshop on Computational Intelligence, pp 183-189. Edimburgo, UK, 10-12 September 2001. [citation][year=2000]Cristea, P. Arsene, A. e Nitulescu, B. (2000). Evolutionary Intelligent Agents. In Proceedings of the Conference on Evolutionary Computation, Special Session on Evolutionary Intelligent Agents (CEC-2000), pp. 1320-1328, San Diego, EUA [publication]Barreiros, J. and Costa, E. and Fonseca, J.A. and Coutinho, F. , "Jitter Reduction in a Real-time Message Transmission System Using Genetic Algorithms", in Congress on Evolutionary Computation 2000, 2000 [citation][year=2009]Mouaaz Nahas, Michael J. Ponta and Michael Shorta (2009). Reducing message-length variations in resource-constrained embedded systems implemented using the Controller Area Network (CAN) protocol. Journal of Systems Architecture Volume 55, Issues 5-6, May-June 2009, Pages 344-354, Elsevier 2009. [citation][year=2008]Grenier, M. Navet, N. (2008). Fine-Tuning MAC-Level Protocols for Optimized Real-Time QoS. IEEE Transactions on Industrial Informatics, pp. 6-15, Vol 4(1), IEEE 2008 [citation][year=2007]Mathieu Grenier, Nicolas Navet (2007). Fine-Tuning MAC-Level Protocols for Optimized Real-Time Quality-of-Service. IRIA Rapport de Recherche, INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE N° 6247, June 2007 [citation][year=2004]C. A. S. Oliveira, P. M. Pardalos, T. M. Querido (2004): Integer Formulations for the Message Scheduling Problem on Controller Area Networks. Chapter of Theory and Algorithms For Cooperative Systems By Don Grundel, Robert Murphey, World Scientific 2004. [citation][year=2003]Thomas Nolte (2003). Reducing pessimism in CAN response time analysis. M¨alardalen Real-Time Research Centre Department of Computer Engineering M¨alardalen University, Vasteras, SWEDEN, 2003. [citation][year=2002]Thomas Nolte and Hanss Hansson and Christer Norström, "Minimizing CAN response-time jitter by message manipulation" [publication]Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "Polymorphy and Hybridization in Genetically Programmed Networks", 2000 [publication]Coutinho, F. and Fonseca, J.A. and Barreiros, J. and Costa, E. , "Using Genetic Algorithms to Reduce Jitter in Control Variables Transmitted over CAN", in International CAN Conference, 2000 [citation][year=2009]Mouaaz Nahas, Michael J. Ponta and Michael Shorta (2009). Reducing message-length variations in resource-constrained embedded systems implemented using the Controller Area Network (CAN) protocol. Journal of Systems Architecture Volume 55, Issues 5-6, May-June 2009, Pages 344-354, Elsevier 2009. [citation][year=2008]Grenier, M. Navet, N. (2008). Fine-Tuning MAC-Level Protocols for Optimized Real-Time QoS. IEEE Transactions on Industrial Informatics, pp. 6-15, Vol 4(1), IEEE 2008 [citation][year=2007]Mathieu Grenier, Nicolas Navet (2007). Fine-Tuning MAC-Level Protocols for Optimized Real-Time Quality-of-Service. IRIA Rapport de Recherche, INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE N° 6247, June 2007 [citation][year=2007]Tao BAI, Zhi-Ming WU (2007). Hybrid Bandwidth Scheduling for CAN-based Networked Control Systems. Acta Automatica Sinica, Volume 33, Issue 9, September 2007. [citation][year=2005]BAI Tao, WU Zhi-ming, YANG Gen-ke (2005): Optimal bandwidth scheduling of networked control systems (NCSs) in accordance with jitter. In Bai et al. / J Zhejiang Univ SCI 2005, 6A(6):535-542, Journal of Zhejiang University SCIENCE, 2005. [citation][year=2003]Thomas Nolte (2003). Reducing pessimism in CAN response time analysis. M¨alardalen Real-Time Research Centre, Department of Computer Engineering M¨alardalen University, Vasteras, SWEDEN, 2003. [citation][year=2002]Thomas Nolte and Hanss Hansson and Christer Norström (2002).Minimizing CAN response-time jitter by message manipulation Proceedings of the 8th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2002), 24-27 September 2002, San Jose, CA, USA. IEEE Computer Society 2002. [citation][year=2002]T. Nolte, Reducing pessimism in CAN response time analysis, Malardalen Real-time Research Centre, Malardalen University, Sweeden, 2002. [publication]Coutinho, F. and Fonseca, J.A. and Barreiros, J. and Costa, E. , "Jitter Minimization with Genetic Algorithms", in IEEE International Workshop on Factory Communication Systems, 2000 [citation][year=2002]Thomas Nolte and Hanss Hansson and Christer Norström (2002).Minimizing CAN response-time jitter by message manipulation Proceedings of the 8th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2002), 24-27 September 2002, San Jose, CA, USA. IEEE Computer Society 2002. 1999(11 publications) [publication]Simões, A. and Costa, E. , "Transposition versus Crossover: An Empirical Study", 1999 [citation][year=2015]Zamdborg, L., Holloway, D. M., Merelo, J. J., Levchenko, V. F., & Spirov, A. V. (2015). Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem. Information sciences, 306, 88-110. [citation][year=2014]Gupta, S. K., & Ramteke, M. (2014). Applications of Genetic Algorithms in Chemical Engineering I: Methodology. In Applications of Metaheuristics in Process Engineering (pp. 39-59). Springer International Publishing. [citation][year=2014]Spirov, A. V., Zagriychuk, E. A., & Holloway, D. M. (2014). Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites. Parallel Processing Letters, 24(02). [citation][year=2013]Larry Bull (2013). Consideration of mobile DNA: new forms of artificial genetic regulatory networks. Natural Computing, pp 1-10, Springer 2013. [citation][year=2013]Larry Bull (2013). On Mobile DNA in Artificial Regulatory Networks: Evolving Functional and Structural Dynamism. arXiv preprint arXiv:1303.7220, 2013. [citation][year=2012]Larry Bull (2012). On Natural Genetic Engineering: Structural Dynamism in Random Boolean Networks. Arxiv preprint arXiv:1201.3545, 2012. [citation][year=2012]Larry Bull (2012). Evolving Boolean Networks with Structural Dynamism. Artificial Life, Pages 1-13, Mit Press, 2012. [citation][year=2011]Zhiwen Yu , Hau-San Wong, Dingwen Wang, Ming Wei (2011). Neighborhood Knowledge-Based Evolutionary Algorithm for Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computation, Volume 15 , Issue 6, pp. 812- 831, IEEE 2011. [citation][year=2010]H. Ishibuchi, N. Tsukamoto, Y. Nojima, (2010). Diversity Improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, Issue 99, IEEE Press, 2010. [citation][year=2010]Emmanuel Ofori Oppong (2010). Optimal resource allocation using knapsack problems a case study of television advertisements at Ghana television (GTV). Mater Thesis, College of Arts and Social Sciences, Kwame Nkrumah Univeristy of Science and Technology, 2010. [citation][year=2009]E. Goldbarg, M. Goldbarg (2009). Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms. Foundations of Computational Intelligence, Volume 3, Global Optimization, Studies in Computational Intelligence, pp. 425-460, Springer 2009. [citation][year=2009]Carlos A. Silva and Eduardo F. Costa (2009). An Algorithm for the Long Run Average Cost Problem for Linear Systems with Non-observed Markov Jump Parameters. Proceedings of the 2009 American Control Conference, pp.4434-4439, Hyatt Regency Riverfront, St. Louis, MO, USA, June 10-12, 2009. [citation][year=2009]Alexander V. Spirov, Alexander B. Kazansky, Leonid Zamdborg, Juan J. Merelo and Vladimir F. Levchenko (2009). Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem. In arxiv.org, 2009. [citation][year=2009]Manojkumar Ramteke, Santosh K. Gupta (2009). Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations. In Gade Pandu Rangaiah (ed), Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, Advances in Process System Engineering, Vol. 1, Chapter 4 , pp. 91-129, World Scientific, 2009. [citation][year=2009]Hui Zhang, Ting-Lin Huang, Wen-Jie He, Application of Heuristic Genetic Algorithm for Optimal Layout of Flow Measurement Stations in Water Distribution Networks. Proceedings of 2009 Fifth International Conference on Natural Computation, vol. 4, pp. 140-143, IEEE, 2009. [citation][year=2009]Raiha, O.;   Koskimies, K.;   Makinen, E. (2009). Empirical study on the effect of crossover in genetic software architecture synthesis. World Congress on Nature & Biologically Inspired Computing, 2009, pp. 619 - 625, IEEE 2009. [citation][year=2009]Yu Han; Yunze Cai; Xiaoming Xu (2009). (C+M) Evolution Algorithm Analysis Based on Optimization Measurement Principle. Fifth International Conference on Natural Computation, 2009. ICNC '09, pp. 547-552, IEEE 2009. [citation][year=2008]Manojkumar Ramteke, Santosh K. Gupta (2008). Multiobjective optimization of an industrial nylon-6 semi batch reactor using the a-jumping gene adaptations of genetic algorithm and simulated annealing. Polymer Engineering and Science, Nov 2008, Business Network. [citation][year=2008]Haritha Metta (2008). Adaptive, Multi-Objective Job Shop Scheduling using Genetic Algorithms. Master thesis on Science in Mechanical Engineering, College of Engineering, University of Kentucky, 2008. [citation][year=2008]X. Zhang, X. Hu, G.Cui, Y. Wang, Y. Niu (2008). An improved shuffled frog leaping algorithm with cognitive behavior. 7th World Congress on Intelligent Control and Automation (WCICA 2008), pp. 6197-6202, IEEE Press 2008. [citation][year=2007]Nga Lam Law, K.Y. Szeto (2007): Adaptive Genetic Algorithm with Mutation and Crossover Matrices. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2330-2333, Hyderabad, India, January 2007. [citation][year=2007]Dinesh K. Khosla, Santosh K. Gupta and Deoki N. Saraf (2007), "Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm.?, Fuel Processing Technology, Vol. 88, Issue 1, pp. 51-63, Elsevier Press, 2007. [citation][year=2007]Y. Chen, TL. Yu, K. Sastry, D.E. Goldberg (2007), "A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms?. Illinois Genetic Algorithms Laboratory, IlliGAL Report Nº 200701, April 2007. [citation][year=2007]B. Sankararao, Santosh K. Gupta (2007): "Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing?. Computers and Chemical Engineering, vol. 31, issue 11, pp. 1496"1515, Elsevier, 2007. [citation][year=2007]Ka Yui Tang (2007). "Enhancing Evolutionary Algorithms Using Transposition?. Bachelor of Science in Computer Information Systems with Honours, University of Bath, 2007. [citation][year=2006]Tomasz Dominic Gwiazda, Genetic Algorithms Reference (Volume I), TG Editor, ISBN 8392395832, 2006. [citation][year=2006]B. Sankararao, Santosh K. Gupta (2006): "Multiobjective optimization of the dynamic operation of an industrial steam reformer using the jumping gene adaptations of simulated annealing?. Asia-Pacific Journal of Chemical Engineering, Volume 1, Issue 1-2 , pp. 21 " 31, Wiley, 2006. [citation][year=2005]McGregor, S. Harvey, I., "Embracing plagiarism: theoretical, biological and empirical justiification for copy operators in genetic optimization", in Genetic Programming and Evolvable Hardware, 6 (4), pp. 407-420. [citation][year=2005]C.N. Meneses, P.M. Pardalos, M.G.C. Resende (2005), "Grasp For Nonlinear Optimization". In A. Lofti (ed.), paper submitted to a journal. [citation][year=2005]Khorsand, A.-R. Akbarzadeh-T, M.-R. (2005): "Quantum gate optimization in a meta-level genetic quantum algorithm?. In IEEE International Conference on Systems, Man and Cybernetics, Vol 4. pp. 3055-3062. [citation][year=2005]Kalin Penev , Guy Littlefair (2005): "Free search: a comparative analysis?, In Information Sciences - Informatics and Computer Science: An International Journal , vol. 172, Issue 1-2, pp. 173 - 193 , 2005. [citation][year=2004]A. Czarn, C. MacNish, K. Vijayan, B. Turlach and R. Gupta, Statistical Exploratory Analysis of Genetic algorithms, IEEE Transactions on Evolutionary Computation, Vol. 8, nº 4, August 2004, pp.405-421. [citation][year=2004]Xinchao Zhao, Xiao-Shan Gao, Zechun Hu, Evolutionary Programming Based on Non-Uniform Mutation, MM Research Preprints, 352{374, MMRC, AMSS, Academia Sinica, No. 23, December 2004 [citation][year=2004]Chun Wai Ma, Kwok Yip Szeto (2004), "Locus Orientated Adaptive Genetic Algorithm: Application to the Zero/One Knapsack Problem". In A. Lofti (ed.), Proceedings of the Fifth International Conference On Recents Advances in Soft Computing, pp 410-415, Nottingham, United Kingdom, 16-18 December, 2004. [citation][year=2003]S. Shervais, M. Zwick (2003), "Ordering Genetic Algorithm Genomes with Reconstructability Analysis?. In International Journal of General Systems, pp. 491-591, Taylor&Francis, 2003. [citation][year=2003]I de Falco, E. Tarantino, A. Della Cioppa (2003): "I meccanismi di mutazione negli Algoritmi Genetici: verso una migliore imitazione della natura?. In Primo Workshop Italiano di Vita Artificiale, Itália, 2003. [citation][year=2002]I. De Falco, A. Della Cioppa and E. Tarantino (2002), "Mutation-based genetic algorithm: performance evaluation", Applied Soft Computing, Volume 1, Issue 4, May 2002, Pages 285-299, 2002. [citation][year=2000]I de Falco, A. Iazzetta, E. Tarantino, A. Della Cioppa (2000), On Biologically Inspired Mutations: The Translocation. In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference (GECCO' 2000), pp. 70-77, Las Vegas, USA, 8-12 July 2000. [citation][year=2000]D. E. Goldberg, J. Borgerson, A. Vaughn, K. Hawley, C. Cunningham, J. Milner, K. Zacarias, B. Wagus, R. Gadient. B. Sutton, M. Pelikan, F. Rothlauf, E. Cantú-Paz (2000), Genetic Algorithms: A Bibliography. IlliGAL Report No. 2000037, December 2000. [publication]Simões, A. and Costa, E. , "Enhancing Transposition Performance", 1999 [citation][year=2013]Li, K., Kwong, S., Wang, R., Tang, K. S., & Man, K. F. (2013). Learning paradigm based on jumping genes: A general framework for enhancing exploration in evolutionary multiobjective optimization. Information Sciences 226, pp 1–22, Elsevier, 2013. [citation][year=2011]Zaheed Ahmed and Irfan Younas (2011). A Dynamic Programming based GA for 0-1 Modified Knapsack Problem. International Journal of Computer Applications 16(7):1–6, Foundation of Computer Science, 2011. [citation][year=2011]M. C. Goldbarg, P. H. Asconavieta da Silva, E. F. G. Goldbarg (2011). Algoritmos Evolucionários na Solução do Problema do Caixeiro Alugador. Lopes and Takahashi (Eds). Computação Evolucionária em Problemas de Engenharia, Capítulo 14, pp. 301-330, ISBN 978-85-94619-00-5, 2011. [citation][year=2011]Ke Li, Sam Kwong, Kim-Fung Man (2011). JGBL paradigm: a novel strategy to enhance the exploration ability of nsga-ii. GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, pp. 99-100, ACM 2011. [citation][year=2009]Alexander V. Spirov, Alexander B. Kazansky, Leonid Zamdborg, Juan J. Merelo and Vladimir F. Levchenko (2009). Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem. In arxiv.org, 2009. [citation][year=2008]T. M. Chan, K. F. Man, S. Kwong, and K. S. Tang (2008). "A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization?. IEEE Transactions on Evolutionary Computation, Vol. 12, 2, pp. 143-159, IEEE Press, April 2008. [citation][year=2008]Haritha Metta (2008). Adaptive, Multi-Objective Job Shop Scheduling using Genetic Algorithms. Master thesis on Science in Mechanical Engineering, College of Engineering, University of Kentucky, 2008. [citation][year=2007]Kazi Shah Nawaz Ripon, Sam Kwong and K.F. Manb (2007). "A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization?. Information Sciences, Volume 177, Issue 2, pp. 632-654, January 2007. [citation][year=2007]S. H. Yeung and K. F. Man (2007): "A New Jumping Genes Paradigm for an E-shaped Folded Patch Feed Antenna Design". International Journal of Microwave Science and Technology, 2007. [citation][year=2007]Ka Yui Tang (2007). "Enhancing Evolutionary Algorithms Using Transposition?. Bachelor of Science in Computer Information Systems with Honours, University of Bath, 2007. [citation][year=2006]Tomasz Dominic Gwiazda, Genetic Algorithms Reference (Volume I), TG Editor, ISBN 8392395832, 2006. [citation][year=2005]McGregor, S. Harvey, I., "Embracing plagiarism: theoretical, biological and empirical justiification for copy operators in genetic optimization", in Genetic Programming and Evolvable Hardware, 6 (4), pp. 407-420. [citation][year=2003]Jarmo T. Alander (2003), Indexed Bibliography of Genetic Algorithm Theory and Comparisons. Report Series No. 94-1-THEORY, Department of Electrical Engineering and Production Economics, University of Vaasa, 2003. [citation][year=2003]Jarmo T. Alander (2003), Indexed bibliography of genetic algorithms in the the Mediterranean Countries. Report Series No. 94-1- MEDITER, Department of Electrical Engineering and Production Economics, University of Vaasa, Finland, 2003. [citation][year=2003]Jarmo T. Alander (2003), Indexed bibliography of genetic algorithms in the Latin America, Portugal and Spain. Report Series No. 94-1-LATIN, Department of Electrical Engineering and Production Economics, University of Vaasa, Finland, 2003. [publication]Simões, A. and Costa, E. , "Transposition: a Biologically Inspired Mechanism to Use with Genetic Algorithms", in ICANNGA, 1999 [citation][year=2015]Zamdborg, L., Holloway, D. M., Merelo, J. J., Levchenko, V. F., & Spirov, A. V. (2015). Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem. Information sciences, 306, 88-110. [citation][year=2014]Gupta, S. K., & Ramteke, M. (2014). Applications of Genetic Algorithms in Chemical Engineering I: Methodology. In Applications of Metaheuristics in Process Engineering (pp. 39-59). Springer International Publishing. [citation][year=2014]Spirov, A. V., Zagriychuk, E. A., & Holloway, D. M. (2014). Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites. Parallel Processing Letters, 24(02). [citation][year=2013]Gupta, S. K., & Garg, S. (2013). Multi-Objective Optimization Using Genetic Algorithm. Control and Optimisation of Process Systems, 43, 205. [citation][year=2013]Santana, R., McKay, R. I., & Lozano, J. A. (2013, June). Symmetry in evolutionary and estimation of distribution algorithms. In Evolutionary Computation (CEC), 2013 IEEE Congress on (pp. 2053-2060). IEEE. [citation][year=2012]Tüze Kuyucu, Ivan Tanev, Katsunori Shimohara (2012). Incremental Evolution of Fast Moving and Sensing Simulated Snake-like Robot with Multiobjective GP and Strongly-typed Crossover. Memetic Computing special issue on Optimization of Complex Systems, Springer, 2012. [citation][year=2011]Terki Amel (2011). Analyse des performances des algorithmes genetiques utilisant differentes techniques d'evolution de la population. Memoire Presenta pour obtenir le diplome de Magister En Electronique, Universita Mentouri Constantine, 2011. [citation][year=2011]Zaheed Ahmed and Irfan Younas (2011). A Dynamic Programming based GA for 0-1 Modified Knapsack Problem. International Journal of Computer Applications 16(7):1–6, Foundation of Computer Science, 2011. [citation][year=2011]Kamil, K. , Chong Kok Hen, Tiong Sieh Kiong, Yeap Kim Ho (2010). Finite persisting sphere Genetic Algorithm in solving multiobjectives problema. 2010 IEEE Student Conference on Research and Development (SCOReD), pp. 183- 186, Putrajaya, 2011. [citation][year=2011]Kamil Karmila, Chong Kok Hen, Tiong Sieh Kiong, Yeap Kim Ho (2011). Application of Crossover Factor on FPSGA in Optimization Problem. 2011 International Conference on Information and Intelligent Computing, IPCSIT 2011, vol.18, IACSIT Press, Singapore, 2011. [citation][year=2010]Jonatan Gomez, Elizabeth León (2010). A Coevolutionary Chromosome Encoding Scheme for High Dimensional Search Spaces. WCCI 2010 IEEE World Congress on Computational Intelligence - IEEE CEC, pp. 55-62, July, 18-23, 2010 - CCIB, Barcelona, Spain, IEEE, 2010. [citation][year=2010]Giovanni Cantor, Jonatan Gómez (2010). Maintaining Genetic Diversity in Fine-grained Parallel Genetic Algorithms by Combining Cellular Automata, Cambrian Explosions and Massive Extinctions. WCCI 2010 IEEE World Congress on Computational Intelligence - IEEE CEC, pp. 3161-3168, July, 18-23, 2010 - CCIB, Barcelona, Spain, IEEE, 2010. [citation][year=2010]Aram Ter-Sarkisov, Stephen R. Marsland, Barbara R. Holland (2010). The K-bit-swap: a new genetic algorithm operator. Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference (GECCO 2010), pp. 815-816, ACM 2010. [citation][year=2009]E. Goldbarg, M. Goldbarg (2009). Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms. Foundations of Computational Intelligence, Volume 3, Global Optimization, Studies in Computational Intelligence, pp. 425-460, Springer 2009. [citation][year=2009]M. Ramteke, S. K. Gupta (2009). Biomimetic Adaptations of GA and SA for the Robust MO Optimization of an Industrial Nylon-6 Reactor. Materials and Manufacturing Processes, Number 24, Vol. 1, pp. 38"46, Taylor & Francis Group, 2009 [citation][year=2009]Alexander V. Spirov, Alexander B. Kazansky, Leonid Zamdborg, Juan J. Merelo and Vladimir F. Levchenko (2009). Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem. In arxiv.org, 2009. [citation][year=2009]Manojkumar Ramteke, Santosh K. Gupta (2009). Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations. In Gade Pandu Rangaiah (ed), Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, Advances in Process System Engineering, Vol. 1, Chapter 4 , pp. 91-129, World Scientific, 2009. [citation][year=2008]Manojkumar Ramteke, Santosh K. Gupta (2008). Multiobjective optimization of an industrial nylon-6 semi batch reactor using the a-jumping gene adaptations of genetic algorithm and simulated annealing. Polymer Engineering and Science, Nov 2008, Business Network. [citation][year=2008]Jonatan Gomez, Giovanni Cantor (2008). Usando Autómatas Celulares como Esquema Poblacional de un Algoritmo Genético. Memoria del 9no. Simposium Iberoamericano de Computación e Informática SICI´08, México, October 2008. [citation][year=2008]Haritha Metta (2008). Adaptive, Multi-Objective Job Shop Scheduling using Genetic Algorithms. Master thesis on Science in Mechanical Engineering, College of Engineering, University of Kentucky, 2008. [citation][year=2008]Jonatan Goméz, Giovani Cantor (2008). An Evolutionary Algorithm Using a Cellular Population Scheme. Tendencias en Ingeniería de Software e Inteligencia Artificial " volumen 2, Capitulo 17, pp. 131 - 138, Universidad Nacional de Colombia, 2008. [citation][year=2007]Kazi Shah Nawaz Ripon, Sam Kwong and K.F. Manb (2007). "A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization?. Information Sciences, Volume 177, Issue 2, pp. 632-654, January 2007. [citation][year=2007]Dinesh K. Khosla, Santosh K. Gupta and Deoki N. Saraf (2007), "Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm.?, Fuel Processing Technology, Vol. 88, Issue 1, pp. 51-63, Elsevier Press, 2007. [citation][year=2007]B. Sankararao, Santosh K. Gupta (2007): "Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing?. Computers and Chemical Engineering, vol. 31, issue 11, pp. 1496"1515, Elsevier, 2007. [citation][year=2007]Kazi Shah Nawaz Ripon (2007). "Hybrid Evolutionary Approach for Multi-Objective Job-Shop Scheduling Problem?, Malaysian Journal of Computer Science, Vol. 20 (2), pp. 183-198, 2007. [citation][year=2007]Ka Yui Tang (2007). "Enhancing Evolutionary Algorithms Using Transposition?. Bachelor of Science in Computer Information Systems with Honours, University of Bath, 2007. [citation][year=2006]Tomasz Dominic Gwiazda, Genetic Algorithms Reference (Volume I), TG Editor, ISBN 8392395832, 2006. [citation][year=2006]N. Agrawal, G. P. Rangaiah, A. K. Ray and S. K. Gupta (2006). Multi-Objective Design Optimization of an Industrial LDPE Tubular Reactor Using Jumping Gene Adaptations of NSGA and Constraint Handling Principle. Industrial Innovation in Process Design & Operations. 2006 Annual Meeting, San Francisco, CA, 2006. [citation][year=2006]B. Sankararao, Santosh K. Gupta (2006): "Multiobjective optimization of the dynamic operation of an industrial steam reformer using the jumping gene adaptations of simulated annealing?. Asia-Pacific Journal of Chemical Engineering, Volume 1, Issue 1-2 , pp. 21 " 31, Wiley, 2006. [citation][year=2005]Martin Zwick, Stephen Shervais (2005). "Ordering genetic algorithm genomes with reconstructability analysis: discrete models.? In IEEE International Conference Systems, Man and Cybernetics, 2005, pp. 3100- 3105 Vol. 4, 2005. [citation][year=2004]Jonatan Gomez (2004), Self Adaptation of Operators Rates in Evolutionary Algorithms. In Kalyanmoy Deb et al. (eds), Proceedings of the 2004 Genetic and Evolutionary Computation Conference, (GECCO' 2004), pp. 1162-1173, Seattle, USA, 26-30 June 2004, LNCS 3102, Springer. [citation][year=2004]Jonatan Gomez (2004) "An Incremental Learning Algorithm for Deterministic Finite Automata using Evolutionary Algorithms?. [citation][year=2004]Martin Zwick, Stephen Shervais (2004), "Reconstructability Analysis Detection of Optimal Gene Order in Genetic Algorithms?. In Kybernetes Journal, ISSN: 0368-492X, Volume 33, Issue: 5/6, pp. 1053 " 1062, Emerald Group Publishing Limited, 2004. [citation][year=2003]S. Shervais, M. Zwick (2003), "Ordering Genetic Algorithm Genomes with Reconstructability Analysis?. In International Journal of General Systems, pp. 491-591, Taylor&Francis, 2003. [citation][year=2002]Martin Zwick, Stephen Shervais (2002), "Reconstructability Analysis Detection of Optimal Gene Order in Genetic Algorithms?. In the 2002 Meeting of the World Organization of Systems and Cybernetics and the International Institute of General Systems Studies. [citation][year=2002]Adnan Acan (2002), "Reciprocal translocation with adaptive segment length?. In Proceedings of the 2002 Congress on Evolutionary Computation, Volume 1, pp. 646-651, 12-17 May, Honolulu, USA, 2002. [publication]Pereira, F.B. and Penousal Machado and Costa, E. and Amilcar Cardoso , "Busy Beaver: an Evolutionary Approach", 1999 [citation][year=2005]Owen Kellet, A Multi-Faceted Attack On The Busy Beaver Problem, M.Sc. Thesis, Rensselaer Polytechnic Institute, Troy, New York, July 2005. [citation][year=2003]S. Bringsjord, M. J. Zenzen. Superminds: People Harness Hypercomputation, and More. Kluwer Academic Publishers [citation][year=2002]Alessandro Perrone and Gianluigi Ferraris "Alessandro Perrone and Gianluigi Ferraris" Intelligent Data Engineering and Automated Learning " IDEAL 2002, Lecture Notes in Computer Science, Volume 2412/2002, 2002. [citation][year=2002]Nattee Niparnan, A Genetic Algorithm for Finite State Machine Inference, PhD Thesis, Chulalongkorn University, 2002. [citation][year=2002]Kyle Ross, Use of Optimisation Techniques in Determining Values for the Quadruplorum Variants of Rado’s Busy Beaver Function, MSc. Thesis, Rensselaer Polytechnic Institute (RPI) 2002. [publication]Pereira, F.B. and Penousal Machado and Costa, E. and Amilcar Cardoso , "Graph-Based Crossover: A Case Study with the Busy Beaver Problem", 1999 [citation][year=2015]A semantic network-based evolutionary algorithm for computational creativity AG Baydin, RL de Mántaras, S Ontañón - Evolutionary Intelligence, 2015 - Springer [citation][year=2012]AG Baydin, RL de Mántaras. Evolution of ideas: A novel memetic algorithm based on semantic networks IEEE Congress on Evolutionary Computation (CEC), 2012. [citation][year=2009]Shalaby, M., Saitou, K. (2009). High-Stifness, Lock-and-Key Heat-Reversible Locator-Snap Systems for the Design for Disassembly. Journal of Mechanical Design, Volume 131, Issue 4, 041005 (9 pages). April 2009 [citation][year=2008]Shalaby, Mohammed Mounir, High-Stiffness, Lock-and-Key Heat-Reversible Locator-Snap Systems for the Design for Disassembly, Ph.D. Thesis, University of Michigan, 2008. [citation][year=2008]Stonedahl, F., Rand, W., and Wilensky, U. 2008. CrossNet: a framework for crossover with network-based chromosomal representations. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (Atlanta, GA, USA, July 12 - 16, 2008). M. Keijzer, Ed. GECCO '08. ACM, New York, NY, 1057-1064. DOI= http://doi.acm.org/10.1145/1389095.1389290 [citation][year=2008]Naidoo A., Pillay N., Using Genetic Programming for Turing Machine Induction, in M. O'Neill et al. (eds.), EuroGP 2008, Lecture Notes in Computer Science 4971, pp. 350 - 361, Springer-Verlag Berlin Heidelberg, 2008. [citation][year=2007]Naidoo A., Pillay N., Evolving Finite Acceptors for Regular Languages, in Neves et al., New Trends in Artificial Intelligence, pp. 193 -206, APPIA, 2007 [citation][year=2006]N. Lyu, B. Lee, and K. Saitou. Partitioning of space frame structures for in-process dimensional adjustability and stiffness. Journal of Mechanical Design, 128(May):527–535, 2006; [citation][year=2006]N. Lyu and K. Saitou. Decomposition-based assembly synthesis of space frame structures using joint library. Journal of Mechanical Design, 128(1):57–65, 2006; [citation][year=2005]Lyu, N., Saitou, K. (2005). Decomposition-Based Assembly Synthesis of a Three-Dimensional Body in White Model for Structural Stiffness. Journal of Mechanical Design, Vol. 127, No.1, pp. 34-48, January 2005 [citation][year=2005]M Shalaby, K Saitou "Design of Heat Reversible Snap Joints for Space Frame Bodies", Proceedings of IDETC/CIE 2005 ASME 2005 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference September 24-28, 2005, Long Beach, California USA [citation][year=2005]N. Lyu and K. Saitou. Topology optimization of multicomponent beam structure via decomposition- based assembly synthesis. Journal of Mechanical Design, 127(2):170–183, 2005; [citation][year=2004]Niehaus, Jens, "Graphbasierte Genetische Programmierung", University of Dortmund, PhD Thesis, 2004. [citation][year=2004]Lyu, N., Saitou, K. (2004). Decomposition-Based Assembly Synthesis of Space Frame Structures using Joint Library. In Proceedings of the International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (DECT"2004), ASME International [citation][year=2004]Lyu, N., Lee, B., Saitou, K. (2004). Decomposition-Based Assembly Synthesis for Structural Stiffness and Dimensional Integrity. In Proceedings of the 2004 ASME International Mechanical Engineering Congress and Exposition (IMECE04), ASME International [citation][year=2004]HA Montes, JL Wyatt, Graph Representation for Program Evolution: an Overview, 2004. [citation][year=2003]Hironobu Katagiri, Pyeongtaek Taro Hiroshi, Takashi Hu reactor, Zyuniti Murata, "Genetic Network Programming Genetic Network Programming accepted variant on the number of nodes", Journal of electricity C (Electronics and Information Systems Division Magazine) Vol. 123 (2003) , No. 1 pp.57-66 Vol. 123 (2003), No. 1 pp.57-66. [citation][year=2003]Yahja, A., Carley, K. (2003). WIZER: What-if Analyzer for Automated Social Model Space Exploration and Validation. In Proceedings of the 2003 North American Association for Computational Society and Organizational Science Conference (NAACSOS 2003). [citation][year=2003]Lyu, N., Saitou, K. (2003). Topology Optimization of Multi-Component Structures Via Decomposition-Based Assembly Synthesis. In Proceedings of the International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (DECT"2003), ASME International. [citation][year=2002]Nattee Niparnan, A Genetic Algorithm for Finite State Machine Inference, PhD Thesis, Chulalongkorn University, 2002. [citation][year=2002]Ben-Amram, A., Petersen, H. (2002). Improved Bounds for Functions Related to Busy Beavers. Theory of Computing Systems, Vol. 35, No. 1, pp. 1-11, Springer-Verlag [citation][year=2002]Katagiri, H., Hirasawa, K., Jinglu, H., Murata, J. (2002). Comparing some Graph Crossover in Genetic Network Programming. In Proceedings of the 41st SICE Annual Conference, Volume 2, pp. 1263-1268, IEEE [citation][year=2002]Dopico, J. R. R, Metodología para el desarrollo de sistemas de extracción de conocimento en RNA. PhD Thesis, University of A Coruña, 2002 [publication]Penousal Machado and Pereira, F.B. and Amilcar Cardoso and Costa, E. , "Busy Beaver - The Influence of Representation", in European Workshop on Genetic Programming, EuroGP\'99, 1999 [citation][year=2011]**Russell, Benjamin James. "Turing Machines and Discrete Time Dynamical Systems." (2011). [citation][year=2008]**Monaco, Vinnie, and Benjamin CS385. "Busy Beaver Genome Project." (2008). [citation][year=2008]**Naidoo, Amashini, and Nelishia Pillay. "Evolving Finite Acceptors for Regular Languages.", EuroGP 2008 [citation][year=2007]**Naidoo A., Pillay N., Evolving Finite Acceptors for Regular Languages, in Neves et al., New Trends in Artificial Intelligence, pp. 193 -206, APPIA, 2007 [citation][year=2004]**Montes, Héctor A., and Jeremy L. Wyatt. Graph representation for program evolution: An overview. University of Birmingham, School of Computer Science, 2004. [citation][year=2004]**Wijers, HJ Michiel. "Bibliography on the Busy Beaver Problem." (2004). [citation][year=2002]Nattee Niparnan, A Genetic Algorithm for Finite State Machine Inference, PhD Thesis, Chulalongkorn University, 2002. [citation][year=2001]**Neri, Filippo. "A study on the effect of cooperative evolution on concept learning." Applications of Evolutionary Computing. Springer Berlin Heidelberg, 2001. 414-420. [citation][year=2000]Santana, Roberto, et al. "Probabilistic Evolution and the Busy Beaver Problem." GECCO. 2000. [publication]Silva, A.P.N.F.d. and Silva, A. and Costa, E. , "Evolutionary Path Planning for Nonholonomic Robots", in GECCO, 1999 [citation][year=2003]Leandro dos Santos Coelho, "Fundamentos, Potencialidades e Aplicações de Algoritmos Evolutivos" , Notas em Matemática Aplicada, Eliana Andrade, Rubens Sampaio, Geraldo Silva (editores) Sociedade Brasileira de Matemática Aplicada e Computacional, São Carlos - SP, Brasil, 2003 [citation][year=2002]Evaluation of Ga-Based Dynamic Route Guidance for Car Navigation Using Cellular Automata, Hitoshi Kanoh, Hideki Kozuka, IEEE Intelligent Vehicle Symposium (IV"2002), pp. (Jun 2002). [citation][year=2000]Route Guidance with Unspecified Staging Posts using Genetic Algorithm for Car Navigation Systems, H. Kanoh, N. Nakamura: IEEE Conference on Intelligent Transportation Systems (ITSC"2000), pp.119-124 (Oct. 2000). [citation][year=2000] Kanoh, H. Nakamura, T. , Knowledge based genetic algorithm for dynamic route selection, Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Volume: 2, On page(s): 616-619 vol.2. [publication]Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "Genetically Programming Networks to Evolve Memory Mechanisms", in GECCO'99, 1999 [citation][year=2006]Kim, D. 2006. Memory analysis and significance test for agent behaviours. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (Seattle, Washington, USA, July 08 - 12, 2006). GECCO '06. ACM, New York, NY, 151-158. [citation][year=2004]Kim, D. (2004). Analyzing Sensor States and Internal States in the Tartarus Problem with Tree State Machines. In (Eds.), Parellel Problem Solving From Nature 8 (Lecture Notes on Computer Science, Vol. 3242, pp. 551-560). : Springer-Verlag [publication]Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "Building Agents with Memory: An Approach using Genetically Programmed Networks", in CEC 99, 1999 [citation][year=2003]J. T. Alander , Indexed Bibliography of Genetic Algorithms and Artificial Intelligence. Report Series No. 94-1-AI, Department of Electrical Engineering and Production Economics, University of Vaasa, 2003. [publication]Silva, A. and Costa, E. , "Evolving Controllers for Autonomous Agents Using Genetically Programmed Networks", in EuroGP 99, 1999 [citation][year=2006]Genetic Programming and Domain Knowledge in Hierarchical Multi-Classification, Richard Llewellyn Smith, Ph.D Thesis, Department of Computer Science, University of Wales, March 2006. [citation][year=2004]Enzyme Genetic Programming - Modelling Biological Evolvability in Genetic Programming, Michael Adam Lones, Department of Electronics, University of York, Heslington, York YO10 5DD, UK, PhD Thesis. Submitted September 2003. Defended March 2004. [citation][year=2003]Genetic Programming in Hardware, Peter N. Martin, Ph.D Thesis, Department of Computer Science, University of Essex, Spring, 2003. [publication]Barreiros, J. and Costa, E. and Pereira, F.B. , "LamBaDa: An Artificial Environment to Study the", in In the Proceedings of the 1999 Congress on Evolutionary Computation (CEC 99), 1999 [citation][year=2006]P.A. Castillo, M.G. Arenas, J.G. Castellano, J.J. Merelo, A. Prieto, V. Rivas, G. Romero (2006). Lamarckian Evolution and the Baldwin Effect in Evolutionary Neural Networks. Neural and Evolutionary Computing 2006. [citation][year=2003]JT Alander (2003). Indexed Bibliography of Genetic Algorithms in the Mediterranean. Adaptive Behavior, 2003 [citation][year=2003]E. X. L. Andrade, R. Sampaio, G. N. Silva (2003). Notas em Matemética Aplicada 2. Universidade Estadual Paulista-UNESP, 2003. 1997(1 publication) [publication]Pereira, F.B. and Costa, E. , "The Influence of Learning in the Optimization of Royal Road Functions", 1997 1996(2 publications) [publication]Paulo Gomes and Bento, C. and Gago, P. and Costa, E. , "Towards a Case-Based Model for Creative Processes", 1996 [citation][year=2004]Matjaz Gams, "Computational Analysis of Human Thinking Processes (Invited Paper)?, International Journal of Computational Cognition (http://www.YangSky.com/yangijcc.htm), Volume 2, Number 3, Pages 1"19, September 2004 [citation][year=1997]Denis Lalanne, George Melissargos and Pearl Pu, "Solving complex problems with computational and interfacing tools?, Swiss workshop on collaborative systems, Mai 1997, EPFL. [publication]Ribeiro, B. and António Dourado and Costa, E. , "Integrated Process Supervision and Control by Neural Networks", in IEEE World Conference on Neural Networks (WCNN96), 1996 [citation][year=1998]A Bayesian"Gaussian neural network and its applications in process engineering H Ye, R Nicolai, L Reh - Chemical Engineering & Processing, 1998 - Elsevier 1995(1 publication) [publication]Ribeiro, B. and António Dourado and Costa, E. , "Lime Kiln fault Detection and Diagnosis by Neural Networks", in International Conference on Artificial Neural Networks and Genetics Algorithms, 1995 [citation][year=2001]A FAULT DIAGNOSTIC MODEL BASED ON NOVEL NEURAL NETWORK CLASSIFIER HE Jia-ZhouiState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093j@ ZHOU Zhi-HuaiState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093j@ GAO YangiState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093j@ CHEN Shi-FuiState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093j Journal of Computer Research and Development 2001 Vol.38 No.1 P.93-97 [citation][year=2000]Using Neural Networks for Fault Diagnosis - He, Zhou, Yin, Chen (2000) Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00), Como, Italy, 2000, vol5pp.217-220. http://citeseer.ist.psu.edu/513306.html 1994(1 publication) [publication]Bento, C. and Costa, E. , "A Similarity Metric for Retrieval of Cases Imperfectly Explained", in Topics in Case-Based Reasoning - Selected Paper from the First European Workshop on Case-Based Reasoning, Kaiserslautern, Springer Verlag., 1994 [citation][year=1998]B. Lemaire, Models of High-dimensional Semantic Spaces, In Proceedings of the 4th International Workshop on MultiStrategy Learning (MSL'98), June 1998. [citation][year=1997]Ramon López de Mántaras, Enric Plaza, Case-Based Reasoning: An Overview, AI Communications, IOS Press. Vol. 10 : 1, pp. 21-29 (1997). [citation][year=1997]Ahmed Almonayyes, A multi-level Indexing Scheme for Retrieving Cases of Multiple Points of View, Fifth German Workshop on Case-Based Reasoning, 1997. [citation][year=1995]Seitz Uhrmacher, Cases versus Model Based Knowledge - An Application in the Area of Bone Healing, Fourth German Workshop on Case-Based Reasoning: System Development and Evaluation. [citation][year=1995]David Leake, "Case-Based Reasoning in context: The present and future", 1995. 1993(1 publication) [publication]Ribeiro, B. and António Dourado and Costa, E. , "A Neural Network based Control of a Simulated Industrial Lime Kiln", in INNS-IEEE International Joint Conference on Neural Networks, 1993 1991(1 publication) [publication]Amilcar Cardoso and Costa, E. , "Time in Confluences: Dealing with Delays for Consistency-Checking", in EPIA'91, 1991 1986(1 publication) [publication]Costa, E. , "Artificial Intelligence and Education: the role of knowledge in teaching", in EWSL, 1986 [citation][year=1989]G. Tecuci, "DISCIPLE: a theory, methodology and system for learning expert knowledge", Thèse de docteur en Sciences, Université de Paris-Sud, 1989. 1980(1 publication) [publication]Costa, E. , "Um método indutivo de transformação de programas", in Primeiro Congresso Português de Informática, 1980 Edited Books 2015(1 publication) [publication]Pereira, F.B. and Penousal Machado and Costa, E. and Amilcar Cardoso , "Progress in Artificial Intelligence – Proceedings of the Seventeenth Portuguese Conference on Artificial Intelligence (EPIA-2015)", vol. 9273, 2015 2007(1 publication) [publication]Costa, F.A.e. and Rocha, L.M. and Costa, E. and Harvey, I. and Coutinho, A. , "Advances in Artificial Life", 2007 2006(1 publication) [publication]Rothlauf, F. and Branke, J. and Cagnoni, S. and Costa, E. and Cotta, C. and Drechsler, R. and Lutton, E. and Penousal Machado and Moore, J. and Romero, J.J. and Smith, G.D. and Squillero, G. and Takagi, H. , "Applications of Evolutionary Computing, EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART and EvoSTOC, Budapest, Hungary, April 10-12, 2006, Proceedings", vol. 3907, 2006 2004(1 publication) [publication]Keijzer, M. and O'Reilly, U. and Lucas, S.M. and Costa, E. and Soule, T. , "Genetic Programming: 7th European Conference", vol. 3003, 2004 [citation][year=2005]K. Sastry, U.-M. O'Reilly, D. E. Goldberg (2005):Population Sizing for Genetic Programming Based Upon Decision Making. Computer Science, Artificial Intelligence, 2005 2003(2 publications) [publication]Quaresma, P. and António Dourado and Costa, E. and JFCosta, . , "Soft Computing and Complex Systems", vol. 1, 2003 [publication]Ryan, C. and Soule, T. and Keijzer, M. and Tsang, E. and Poli, R. and Costa, E. , "Genetic Programming: 6th European Conference", vol. 2610, 2003 1997(1 publication) [publication]Costa, E. and Amilcar Cardoso , "Progress in Artificial Intelligence, LNCS, Vol. 1323", vol. 1, 1997 Book Chapters 2018(1 publication) [publication]Lourenço, Nuno and Assunção, F. and Pereira, F.B. and Costa, E. and Penousal Machado , "Structured Grammatical Evolution: A Dynamic Approach", in Handbook of Grammatical Evolution, vol. 1, pp. 1-24, 2018 2008(1 publication) [publication]Goncalves, A. and Costa, E. , "A model for an heterogeneous gene regulatory network", in Computational Methodologies for Gene Regulatory Networks, 2008 2006(3 publications) [publication]Brabazon, A. and Silva, A. and Costa, E. and Sousa, T. and O'Neill, M. , "Simulating the strategic adaptation of organizations using OrgSwarm", in Handbook of bioinspired algorithms and applications, pp. 305-319, 2006 [citation][year=2010]Soumya Banerjee, Hamid R. Tizhoosh (2010).Visualization of Hidden Structures in Corporate Failure Prediction Using Opposite Pheromone per Node Model. WCCI 2010 IEEE World Congress on Computational Intelligence - IEEE CEC, pp. 4624-4628, July, 18-23, 2010 - CCIB, Barcelona, Spain, IEEE, 2010. [publication]Sousa, T. and Silva, A. and Silva, A.P.N.F.d. and Costa, E. , "Bioinspired data mining", in Handbook of bioinspired algorithms and applications, pp. 469-489, 2006 [publication]Brabazon, A. and Silva, A. and Sousa, T. and O'Neill, M. and Matthews, R. and Costa, E. , "Simulating Product Invention using InventSim", in Handbook of Research on Nature Inspired Computing for Economy and Management, pp. 379-394, 2006 2004(1 publication) [publication]Penousal Machado and Pereira, F.B. and Tavares, J. and Costa, E. and Amilcar Cardoso , "Evolutionary turing machines – the quest for busy", in Recent Developments in Biologically Inspired Computing , vol. 1, pp. 9-40, 2004 2001(1 publication) [publication]Amilcar Cardoso and Costa, E. and Penousal Machado and Pereira, F.C. and Paulo Gomes , "An architecture for hybrid creative reasoning", in Soft computing in case based reasoning, vol. 1, pp. 147-177, 2001 [citation][year=2015]. Social eedback as a reative rocess CP F M d'Inverno, A Still - Music Learning with Massive Open Online …, 2015 - books.google.com [citation][year=2014]d'Inverno, Mark, and Arthur Still. "Creative Feedback: a Manifesto for Social Learning." EDM (Workshops). 2014. [citation][year=2014]Schmidt-Thieme, Lars, and Ruth Janning. "Workshop on Feedback from Multimodal Interactions in Learning Management Systems (FFMI)." [citation][year=2012]**Segond, Marc, and Christian Borgelt. "Selecting the links in bisoNets generated from document collections." Bisociative Knowledge Discovery. Springer Berlin Heidelberg, 2012. 54-65. [citation][year=2010]Marc Segond, Christian Borgelt: Selecting the Links in BisoNets Generated from Document Collections. Intelligent Data Analysis IX, 9th International Symposium IDA 2010: 196-207. LNCS. Springer. [citation][year=2009]**Segond, Marc, and Christian Borgelt. "“BisoNet” Generation using textual data." Workshop on Explorative Analytics of Information Networks at ECML PKDD 2009. 2009. 1994(1 publication) [publication]Gaspar, G. and Costa, E. and Coelho, H. , "Student modelling: the key to individualized knowledge-based instruction", in Student modelling: the key to individualized knowledge-based instruction, 1994 [citation][year=1994]Flavio Moreira, Equilibration and belief revision: strategies for cooperative tutoring and learning, Proceedings of the 13th international conference on Computer science 2 : research and applications: research and applications, Plenum Press New York, NY, USA, 1994. 1992(2 publications) [publication]Costa, E. and Urbano, P. , "Machine Learning, Explanation-Based Learning and Intelligent Tutoring Systems", in New directions for intelligent tutoring systems, pp. 91-106, 1992 [citation][year=1992]Pierre Dillenbourg and John Self , A Framework for Learner Modelling (1990), in Glaser (ed.), Advances in Instructional Psychology II, Hillsdale, N.J.: Erlbaum. Also in Interactive Learning Environments, 2 (2), 111-137 (1992). [publication]Costa, E. , "New directions in educational technology", in New directions in educational technology, 1992 1988(1 publication) [publication]Costa, E. and Duchénoy, S. and Kodratoff, Y. , "A resolution-based method for discovering student misconceptions", in Artificial Intelligence and Human Learning, pp. 156-164, 1988 [citation][year=1998]R. Sison, M. Shimura, Student modeling and machine learning, in International journal of Artificial Intelligence in -education (1998), 9, 128-158. [citation][year=1996]Paul Baffes, Refinement-Based Student Modeling and Automated Bug Library Construction, Journal of Artificial Intelligence in Education, 7,1 (1996) pp. 75-116 [citation][year=1996]R. Sison and M. Shimura, The application of Machine Learning to student modelling: survey and analysis, Department of Computer Science, Tokyo Insstitute of Technology, TR96-0010, June, 1996. [citation][year=1995]John Self, Computational Mathetics, draft, University of Leeds, 1995. [citation][year=1993]D. Nichols, Intelligent Student systems: an application of view points to intelligent learning environments, PhD Thesis, University of Lancaster, UK, 1993 [citation][year=1991]John Self , Formal Approaches to Student Modelling (1991), in McCalla, G.I. and Greer, J. (eds.), Student Modelling: the key to individualized knowledge-based instruction Springer-Verlag, 1991. [citation][year=1989]R. Viccari, "Um tutor inteligente para a programação em lógica: idealização, projecto e desenvolvimento", Tese de Doutoramento, Universidade de Coimbra, 1989. 1987(1 publication) [publication]Duchénoy, S. and Costa, E. and Kodratoff, Y. , "Intelligent Computer-aided Instruction", in Intelligent Computer-aided Instruction, 1987 PhD Theses 1985(1 publication) [publication]Costa, E. , "Estudo e Implementação de um Sistema Automático de Transformação de Programas Recursivos", 1985 [citation][year=1987]N. Azibi, "TREQUASI: un système pour la transformation automatique de programmes Prolog récursifs en quasi-itératifs", Thèse de Docteur en Science, Université de Paris-Sud,1987. 1981(1 publication) [publication]Costa, E. , "Dérécursivation Automatique en Utilisant des Systèmes de Réecriture de Termes", 1981 [citation][year=1987]N. Azibi, "TREQUASI: un système pour la transforamation automatique de programmes Prolog récursifs en quasi-itératifs", Thèse de Docteur en Science, Université de Paris-Sud,1987. MSc Theses 1979(1 publication) [publication]Costa, E. , "Des Méthodes de Transformation de Programmes", 1979 Tech Report 2009(1 publication) [publication]Simões, A. and Costa, E. , "Prediction in Evolutionary Algorithms for Dynamic Environments using Markov Chains and Nonlinear Regression", 2009 2008(3 publications) [publication]Simões, A. and Costa, E. , "The Influence of Population and Memory Sizes on the Evolutionary Algorithm's Performance for Dynamic Environments", 2008 [publication]Simões, A. and Costa, E. , "Evolutionary Algorithms for Dynamic Environments: Prediction using Linear Regression and Markov Chains", 2008 [publication]Simões, A. and Costa, E. , "Evaluating Predictor\'s Accuracy in Evolutionary Algorithms for Dynamic Environments", 2008 2007(6 publications) [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Multidimensional Knapsack Problem: The Influence of Representation", 2007 [citation][year=2007]ÿzcan, E. and Başaran, C. 2009. \textbf{A case study of memetic algorithms for constraint optimization}. Soft Comput. 13, 8-9 (Mar. 2009), 871-882. [publication]Leitão, T. and Pereira, F.B. and Marques, J.C. and Tavares, J. and Costa, E. , "Niching Techniques: a Study on the Cluster Geometry Optimization Problem", 2007 [publication]Simões, A. and Costa, E. , "Variable-size Memory Evolutionary Algorithm: Studies on the impact of different replacing strategies in the algorithm's performance and in the population's diversity when dealing with dynamic environments", 2007 [publication]Simões, A. and Costa, E. , "Improving Memory-based Evolutionary Algorithms in Changing Environments", 2007 [citation][year=2009]J. Tim Hendtlass, Irene Moser, Marcus Randal (2009). Dynamic Problems and Nature Inspired Meta-heuristics. Biologically-Inspired Optimisation Methods , Series Studies in Computational Intelligence, Volume 210, pp. 79-109, Springer 2009. [publication]Simões, A. and Costa, E. , "Using Linear Regression to Predict Changes in Evolutionary Algorithms dealing with Dynamic Environments", 2007 [publication]Tavares, J. and Pereira, F.B. and Costa, E. , "Golomb Rulers: the Influence of Representation and Heuristics", 2007 2006(1 publication) [publication]Simões, A. and Costa, E. , "Variable-size Memory Evolutionary Algorithm to Deal with Dynamic Environments: an empirical study", 2006 1999(1 publication) [publication]Simões, A. and Costa, E. , "Using Genetic Algorithms with Asexual Transposition", 1999