Books 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 Journal Articles 2014(1 publication) [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. Conference Articles 2013(2 publications) [publication]Dinis, R. and Simões, A. and Jorge Bernardino , "GraphEA: A 3D Educational Tool for Genetic Algorithms", in Proceedings of the 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), Christian Blum (Ed.), pp. 1293-1300, Amsterdam, The Netherlands, 06-10, ACM, New York, NY, USA, 2013., 2013 [citation][year=2015]Cruz, A., Machado, P., Assunção, F., & Leitão, A. (2015, July). ELICIT: Evolutionary Computation Visualization. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (pp. 949-956). ACM. [citation][year=2014]Koon, E. (2014). Assembling 3D Objects with Artificial Spatial Intelligence. [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 2012(2 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]Simões, A. and Costa, E. , "Enhancing the Virtual Loser Genetic Algorithm for Dynamic Environments", in Genetic and Evolutionary Computation Conference (GECCO 2012), 2012 2011(3 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]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. 2009(3 publications) [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. 2008(1 publication) [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. 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[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. 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