Journal Articles 2020(2 publications) [publication]Schulze, B. and Stiglmayr, M. and Paquete, L. and Fonseca, C.M. and Willems, D. and Ruzika, S. , "On the rectangular knapsack problem - approximation of a specific quadratic knapsack problem", Mathematical Methods of Operations Research (to appear), 2020 [publication]Andreia P. Guerreiro and Fonseca, C.M. , "An analysis of the hypervolume Sharpe-ratio indicator", European Journal of Operational Research, vol. 283, pp. 614-629, 2020 2018(2 publications) [publication]Andreia P. Guerreiro and Fonseca, C.M. , "Computing and updating hypervolume contributions in up to four dimensions", IEEE Transactions on Evolutionary Computation., vol. 22, pp. 449-463, 2018 [publication]Andre Riker and Fonseca, C.M. and Marilia Curado and Edmundo Monteiro , "Energy-efficient multigroup communication", Emerging Telecommunications Technologies, vol. 29, 2018 2017(3 publications) [publication]Lacour, R. and Klamroth, K. and Fonseca, C.M. , "A Box Decomposition Algorithm to Compute the Hypervolume Indicator", Computers & Operations Research, vol. 79, pp. 347-360, 2017 [publication]Figueira, J. and Fonseca, C.M. and Halffmann, P. and Klamroth, K. and Paquete, L. and Ruzika, S. and Schulze, B. and Stiglmayr, M. and Willems, D. , "Easy to say they are Hard, but Hard to see they are Easy-Towards a Categorization of Tractable Multiobjective Combinatorial Optimization Problems", Journal of Multi-Criteria Decision Analysis, vol. 24, pp. 82-88, 2017 [publication]Ferreira, J.C. and Fonseca, C.M. and Denysiuk, R. and Gaspar?Cunha, A. , "Methodology to select solutions for multiobjective optimization problems: Weighted stress function method", Journal of Multi-Criteria Decision Analysis, vol. 24, pp. 103-120, 2017 2016(2 publications) [publication]Kuhn, T. and Fonseca, C.M. and Paquete, L. and Ruzika, S. and Duarte, M.M. and Figueira, J. , "Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms", Evolutionary Computation, vol. 24, 2016 [citation][year=2016]Michael Emmerich, André Deutz, Longmei Li, Asep Maulana, Iryna Yevseyeva, Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making, Procedia Computer Science, Volume 100, 2016, Pages 848-855 [citation][year=2016]Hisao Ishibuchi ; Yu Setoguchi ; Hiroyuki Masuda ; Yusuke Nojima, How to compare many-objective algorithms under different settings of population and archive sizes, Evolutionary Computation (CEC), 2016 IEEE Congress on, 2016 [citation][year=2015]Dimo Brockhoff, Thanh-Do Tran, Nikolaus Hansen. Benchmarking Numerical Multiobjective Optimizers Revisited. A.I. Esparcia and S. Silva. Genetic and Evolutionary Computation Conference (GECCO 2015), Jul 2015, Madrid, Spain. pp.639-646, <10.1145/2739480.2754777>. [citation][year=2015]MTM Emmerich, AH Deutz, I Yevseyeva A Bayesian Approach to Portfolio Selection in Multicriteria Group Decision Making Procedia Computer Science, 2015 - Elsevier [citation][year=2015]T Friedrich, F Neumann Maximizing submodular functions under matroid constraints by evolutionary algorithms Evolutionary computation, 2015 [publication]Andreia P. Guerreiro and Fonseca, C.M. and Paquete, L. , "Greedy Hypervolume Subset Selection in Low Dimensions", Evolutionary Computation, vol. 24, 2016 2015(1 publication) [publication]Vaz, D. and Paquete, L. and Fonseca, C.M. and Klamroth, K. and Stiglmayr, M. , "Representation of the non-dominated set in biobjective discrete optimization", Computers & Operations Research, 2015 [citation][year=2016]Lizhen Shao, Matthias Ehrgott, Discrete representation of non-dominated sets in multi-objective linear programming, European Journal of Operational Research, Volume 255, Issue 3, 16 December 2016, Pages 687-698 [citation][year=2016]Daniel Jornada, V. Jorge Leon, Biobjective robust optimization over the efficient set for Pareto set reduction, European Journal of Operational Research, Volume 252, Issue 2, 16 July 2016, Pages 573-586, ISSN 0377-2217 [citation][year=2016]Cristina Bazgan, Florian Jamain, Daniel Vanderpooten, Discrete representation of the non-dominated set for multi-objective optimization problems using kernels, European Journal of Operational Research, Available online 12 November 2016. [citation][year=2016]S. Razavyan, A Method for Generating a Well-Distributed Pareto Set in Multiple Objective Mixed Integer Linear Programs Based on the Decision Maker's Initial Aspiration Level, Asia Pac. J. Oper. Res. 33, 1650031 (2016) 2014(1 publication) [publication]Martins, J.P. and Fonseca, C.M. and Delbem, A.C. , "On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem", Neurocomputing, vol. 146, pp. 17-29, 2014 [citation][year=2015]Jinwei Niu, Weimin Zhong,Yi Liang, Na Luo, Feng Qian, "Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization," volume 88, November 2015, pages 253–263, November 2015. http://dx.doi.org/10.1016/j.knosys.2015.07.027 [citation][year=2015]Jayanthi Manicassamy, S. Sampath Kumar, Mohana Rangan, V. Ananth, T. Vengattaraman, P. Dhavachelvan, "Gene Suppressor: An added phase toward solving large scale optimization problems in genetic algorithm," Applied Soft Computing, volume 35, pages 214–226, October 2015. http://dx.doi.org/10.1016/j.asoc.2015.06.017 [citation][year=2015]Shih-Huan Hsu and Tian-Li Yu, "Handling Overlap by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II," TEIL Technical Report No. 2015002, February 2015. 2009(1 publication) [publication]Beume, N. and Fonseca, C.M. and López-Ibáñez, M. and Paquete, L. and Vahrenhold, J. , "On the complexity of computing the hypervolume indicator", IEEE Transactions on Evolutionary Computation, vol. 13, pp. 1075-1082, 2009 [citation][year=2015]Sen Bong Gee; Kay Chen Tan; Vui Ann Shim; Pal, N.R., "Online Diversity Assessment in Evolutionary Multiobjective Optimization: A Geometrical Perspective," in Evolutionary Computation, IEEE Transactions on , vol.19, no.4, pp.542-559, Aug. 2015 doi: 10.1109/TEVC.2014.2353672 [citation][year=2015]Ran Cheng; Yaochu Jin; Narukawa, K.; Sendhoff, B., "A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling," in Evolutionary Computation, IEEE Transactions on , vol.19, no.6, pp.838-856, Dec. 2015 doi: 10.1109/TEVC.2015.2395073 [citation][year=2015]Dimo Brockhoff, Tobias Wagner, and Heike Trautmann, "2 Indicator-Based Multiobjective Search", Evolutionary Computation 2015 23:3, 369-395 [citation][year=2015]Leonardo C. T. Bezerra , Manuel López-Ibáñez, Thomas Stützle, "Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms," Evolutionary Multi-Criterion Optimization2015, Volume 9018 of the series Lecture Notes in Computer Science pp 396-410. [citation][year=2015]T. Barthélémy, S. N. Parragh and F. Tricoire, Beam search for integer multi-objective optimization, Working paper, 2015. http://www.optimization-online.org/DB_FILE/2015/04/4879.pdf [citation][year=2015]JS Angelo, HS Bernardino, HJC Barbosa, Ant colony approaches for multiobjective structural optimization problems with a cardinality constraint, Advances in Engineering Software Volume 80, February 2015, Pages 101–115 [citation][year=2015]A Bayesian Approach to Portfolio Selection in Multicriteria Group Decision Making MTM Emmerich, AH Deutz, I Yevseyeva Procedia Computer Science, 2015 [citation][year=2015]Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm M Rajabi-Bahaabadi, A Shariat-Mohaymany, Expert Systems with Applications, 2015 [citation][year=2015]Cost to Evaluate Versus Cost to Learn? Performance of Selective Evaluation Strategies in Multiobjective Optimization KS Bhattacharjee, T Ray AI 2015: Advances in Artificial Intelligence, 2015 [citation][year=2015]Faster exact algorithms for computing expected hypervolume improvement I Hupkens, A Deutz, K Yang, M Emmerich Evolutionary Multi-Criterion Optimization, 2015 [citation][year=2015]Performance of a steady state quantum genetic algorithm for multi/many-objective engineering optimization problems Asafuddoula, M. ; Ray, T. ; Isaacs, A. ; Singh, H.K. Evolutionary Computation (CEC), [citation][year=2015]Selective evaluation in multiobjective optimization: A less explored avenue KS Bhattacharjee, T Ray - Evolutionary Computation (CEC), 2015 [citation][year=2015]A new uniform evolutionary algorithm based on decomposition and CDAS for many-objective optimization D Cai, W Yuping Knowledge-Based Systems 2015 [citation][year=2015]Performance metrics in multi-objective optimization Riquelme, Nery; von Lucken, Christian ; Baran, Benjamin Computing Conference (CLEI), 2015 Latin American [citation][year=2015]General subpopulation framework and taming the conflict inside populations DV Vargas, J Murata, H Takano, ACB Delbem Evolutionary computation, 2015 [citation][year=2015]A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization C Dai, Y Wang Applied Soft Computing, 2015 [citation][year=2015]Ant colony approaches for multiobjective structural optimization problems with a cardinality constraint JS Angelo, HS Bernardino, HJC Barbosa Advances in Engineering Software, 2015 [citation][year=2015]S-metric based multi-objective fireworks algorithm L Liu, S Zheng, Y Tan Evolutionary Computation (CEC), 2015 [citation][year=2015]S-Metric-Based Multi-objective Fireworks Algorithm Y Tan Fireworks Algorithm, 2015 [citation][year=2015]Hybrid Algorithm for Multi-Objective Optimization by Greedy Hypervolume Maximization CS Miranda, FJ Von Zuben arXiv, 2015 [citation][year=2015]Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks CS Miranda, FJ Von Zuben arXiv 2015 [citation][year=2015]An improved {\ alpha}-dominance strategy for many-objective optimization problems C Dai, Y Wang, L Hu Soft Computing, 2015 [citation][year=2015]Decomposition-based chemical reaction optimization (CRO) and an extended CRO algorithms for multiobjective optimization H Li, L Wang, X Hei Journal of Computational Science, 2015 [citation][year=2015]Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach A Hidalgo-Paniagua, MA Vega-Rodríguez, J Ferruz Soft Computing, 2015 [citation][year=2015]Learning to Anticipate Flexible Choices in Multiple Criteria Decision-Making Under Uncertainty CRB Azevedo, FJ Von Zuben Cybernetics, IEEE Transactions on, 2015 [citation][year=2015]Adaptive Computation of the Klee's Measure in High Dimensions J Barbay, P Pérez-Lantero arXiv, 2015 [citation][year=2015]Optimizacion de enjambre de particulas para problemas de muchos objetivos M Torres, B Baran Computing Conference (CLEI), 2015 Latin America, 2015 [citation][year=2015]Cai Dai, Yuping Wang, Lijuan Hu, "An improved alpha-dominance strategy for many-objective optimization problems", Soft Computing, doi:10.1007/s00500-014-1570-8, published online 01 January 2015. [citation][year=2014]M. Emmerich and A. Deutz, “Time complexity and zeros of the hypervolume indicator gradient field,” in EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III (O. Schuetze, C. A. Coello Coello, A.-A. Tantar, E. Tantar, P. Bouvry, P. Del Moral, P. Legrand, eds.), vol. 500 of Studies in Computational Intelligence, pp 169-193, Berlin: Springer, 2014. [citation][year=2014]Rui Wang, Peter J. Fleming and Robin C. Purshouse, "General framework for localised multi-objective evolutionary algorithms," Information Sciences, Vol. 258, Pages 29–53, 2014. [citation][year=2014]K Bringmann, T Friedrich, Two-dimensional Subset Selection for Hypervolume and Epsilon-Indicator, GECCO 2014 [citation][year=2014]F Gu, HL Liu, KC Tan, A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment, Soft Computing, 2014 [citation][year=2014]T Tušar, B Filipic, Visualizing Exact and Approximated 3D Empirical Attainment Functions, Mathematical Problems in Engineering, 2014 [citation][year=2014]W Wang, Multi-objective sequential decision making, PhD thesis, 2014 [citation][year=2014]MTM Emmerich, AH Deutz, I Yevseyeva, On Reference Point Free Weighted Hypervolume Indicators based on Desirability Functions and their Probabilistic Interpretation, Procedia Technology, 2014 [citation][year=2014]S Jiang, YS Ong, J Zhang, L Feng, Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization,IEEE Transactions on Cybernetics, 44(12), 2391 - 2404 , 2014 [citation][year=2014]I Hupkens, M Emmerich, A Deutz, Faster Computation of Expected Hypervolume Improvement, arXiv preprint arXiv:1408.7114, 2014 [citation][year=2014]K Nowak, M Märtens, D Izzo, Empirical Performance of the Approximation of the Least Hypervolume Contributor, Parallel Problem Solving from Nature – PPSN XIII, Lecture Notes in Computer Science Volume 8672, 2014, pp 662-671 [citation][year=2014]N Boland, H Charkhgard, M Savelsbergh, The L-Shape Search Method for Triobjective Integer Programming, Publication/NA, 2014 [citation][year=2014]I Tsoukalas, C Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling & Software, 2014 [citation][year=2013]Iris Hupkens, “Complexity Reduction and Validation of Computing the Expected Hypervolume Improvement,” Master's thesis, Universiteit Leiden, Opleiding Informatica. Internal Report 2013–12, August 2013. [citation][year=2013]Rui Wang, “Preference-inspired Co-evolutionary Algorithms,” PhD thesis, University of Sheffield, 2013. [citation][year=2013]Koji Shimoyama, Koma Sato, Shinkyu Jeong and Shigeru Obayashi, "Updating Kriging Surrogate Models Based on the Hypervolume Indicator in Multi-Objective Optimization," J. Mech. Des. 135(9), 094503, 2013. [citation][year=2013]Heike Trautmann, Tobias Wagner, Dirk Biermann, Claus Weihs, Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index, Journal of Multicriteria Decision Analysis, 20(5-6), 319,337, 2013 [citation][year=2013]I Couckuyt, D Deschrijver, T Dhaene, Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization, Journal of Global Optimization, 2013 [citation][year=2013]R Wang, RC Purshouse, Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, 17(4) 2013 [citation][year=2013]P Esling, C Agon, Multiobjective time series matching for audio classification and retrieval, IEEE transaction on Audio, Speech, and Language Processing, 2013 [citation][year=2013]W Wang, M Sebag, Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search, Machine learning, 2013 [citation][year=2013]I Couckuyt, D Deschrijver, T Dhaene, Fast calculation of multiobjective probability of improvement and expected improvement criteria for pareto optimization, Journal of Global Optimization, 2013 [citation][year=2013]K Shimoyama, S Jeong, Kriging-surrogate-based optimization considering expected hypervolume improvement in non-constrained many-objective test problems, CEC 2013. [citation][year=2013]R Wang, Preference-inspired co-evolutionary algorithms, PhD Thesis, 2013 [citation][year=2013]AA BAKHSH, A POSTERIORI AND INTERACTIVE APPROACHES FOR DECISION-MAKING WITH MULTIPLE STOCHASTIC OBJECTIVES, Publication/NA, 2013 [citation][year=2013]Shimoyama, Koji , Jeong, Shinkyu, Obayashi, Shigeru, Kriging-surrogate-based optimization considering expected hypervolume improvement in non-constrained many-objective test problems, 2013 IEEE Congress on Evolutionary Computation (CEC), 658 - 665, 2013. [citation][year=2013]Iris Hupkens, Michael Emmerich, Logarithmic-Time Updates in SMS-EMOA and Hypervolume-Based Archiving, EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV, Advances in Intelligent Systems and Computing Volume 227, 2013, pp 155-169 [citation][year=2013]Ray, Tapabrata , Asafuddoula, Md, Isaacs, Amitay, A steady state decomposition based quantum genetic algorithm for many objective optimization, , 2013 IEEE Congress on Evolutionary Computation (CEC), 2817 - 2824, 2013. [citation][year=2013]Weijia Wang, Michèle Sebag, Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search, Machine Learning September 2013, Volume 92, Issue 2-3, pp 403-429 [citation][year=2013]D. H. Phan and J. Suzuki, "R2-IBEA: R2 Indicator Based Evolutionary Algorithm for Multiobjective Optimization," In Proc. of IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, June 2013. [citation][year=2013]Hans J. F. Moen, Nikolai B. Hansen, Harald Hovland, Jim Tørresen , Many-Objective Optimization Using Taxi-Cab Surface Evolutionary Algorithm, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science Volume 7811, pp 128-142, 2013 [citation][year=2013]Michael Emmerich, André Deutz, Johannes Kruisselbrink, Pradyumn Kumar Shukla, Cone-Based Hypervolume Indicators: Construction, Properties, and Efficient Computation, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science Volume 7811, pp 111-127, 2013 [citation][year=2013]K. Bringmann, “Bringing Order to Special Cases of Klee’s Measure Problem,” in Mathematical Foundations of Computer Science 2013 (K. Chatterjee and J. Sgall, eds.), vol. 8087 of Lecture Notes in Computer Science, pp 207-218, Berlin: Springer, 2013. [citation][year=2013]MG Villarreal-Marroquín, JD Svenson, A comparison of two metamodel-based methodologies for multiple criteria simulation optimization using an injection molding case study, Journal of Polymer Engineering, 2013 [citation][year=2013]K. Bringmann, T. Friedrich, Parameterized Average-Case Complexity of the Hypervolume Indicator, Proc. of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013), Amsterdam, The Netherlands, ACM, 2012. [citation][year=2012]M Helbig, Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation, PhD thesis, 2012 [citation][year=2012]P CARDOSO,M.Jesus, P. Guerreiro, P. Ribeiro, A. Marquez, J. Porillo, Two multi-criteria evolutionary algorithms for a multi-path evacuation problem, The 6th WSEAS Conference, 2012 [citation][year=2012]L. While, L. Bradstreet, L. Barone, A Fast Way of Calculating Exact Hypervolumes, IEEE Transactions on Evolutionary Computation, 2012. [citation][year=2012]Karl Bringmann, Tobias Friedrich, Approximating the least hypervolume contributor: NP-hard in general, but fast in practice, Theoretical Computer Science, 2012 [citation][year=2012]A Auger, J Bader, D Brockhoff, E Zitzler, Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications, Theoretical Computer Science, 2012 [citation][year=2012]M Díez-Fernández, S Teleña, D Gorse, Construction of Emerging Markets Exchange Traded Funds Using Multiobjective Particle Swarm Optimisation, Artificial Neural Networks and Machine Learning – ICANN 2012, LNCS 7553, 2012, pp 140-147 [citation][year=2012]MM Drugan, D Thierens, Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies, Journal of Heuristics, 2012 [citation][year=2012]L While, L Bradstreet, Applying the WFG algorithm to calculate incremental hypervolumes, Evolutionary Computation Conference, 2012 [citation][year=2012]W Wang, M Sebag, Multi-objective Monte-Carlo Tree Search, Asian Conference on Machine Learning, 2012 [citation][year=2012]K Shimoyama, K Sato, S Jeong, Comparison of the criteria for updating Kriging response surface models in multi-objective optimization, Evolutionary Computation Conference, 2012 [citation][year=2012]I Couckuyt, D Deschrijver, Towards Efficient Multiobjective Optimization: Multiobjective statistical criterions, Evolutionary Computation Conference, 2012 [citation][year=2012]D Brockhoff, T Wagner, H Trautmann, On the Properties of the R2 Indicator, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, 2012 [citation][year=2011]MTM Emmerich, CM Fonseca, Computing hypervolume contributions in low dimensions: Asymptotically optimal algorithm and complexity results, Evolutionary Multi-Criterion Optimization, 2011 [citation][year=2011]W Zhu, A Yaseen, Y Li, DEMCMC-GPU: an efficient multi-objective optimization method with GPU acceleration on the fermi architecture, New Generation Computing, 2011 [citation][year=2011]L Bradstreet, The Hypervolume Indicator for Multi-objective Optimisation: Calculation and Use, PHD thesis, 2011 [citation][year=2011]Z He, Performance metrics ensemble for multiobjective evolutionary algorithms, PhD thesis, 2011 [citation][year=2011]Johannes Bader, Eckart Zitzler, HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization, Evolutionary Computation, Vol. 19, No. 1, 45-76, 2011. [citation][year=2011]B Naujoks, Design and tuning of an evolutionary multiobjective optimisation algorithm, PhD thesis, TU Dortmund, Germany, 2011 [citation][year=2011]Weihang Zhu, Ashraf Yaseen and Yaohang Li, DEMCMC-GPU: An Efficient Multi-Objective Optimization Method with GPU Acceleration on the Fermi Architecture, New Generation Computing, Volume 29, Number 2, 163-184, 2011 [citation][year=2011]D. Brockhoff, Theoretical Aspects of Evolutionary Multiobjective Optimization, A. Auger, B. Doerr (eds), Theory of Randomized Algorithms, World Scientific, 2011 [citation][year=2011]J. P. Arun, A radius based guide selection technique in multi-objective particle swarm optimization, IEEE International Conference on Recent Trends in Information Technology, pp. 1169-1174, 2011 [citation][year=2011]A. P. Guerreiro, Efficient Algorithms for the Assessment of Stochastic Multiobjective Optimizers, Master's thesis, Instituto Superior Técnico, 2011. [citation][year=2010]T Lust, New metaheuristics for solving MOCO problems: application to the knapsack problem, the traveling salesman problem and IMRT optimization, PhD thesi, 2010 [citation][year=2010]T Meinl, Maximum-score diversity selection., PhD thesis, 2010 [citation][year=2010]H Ishibuchi, Y Sakane, Y Nojima, Use of Multiple Grids with Different Scalarizing Functions in MOEA/D, SCIS & ISIS, 2010 [citation][year=2010]T. Wagner, H. Trautmann, Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions, IEEE Transactions on Evolutionary Computation, 14 (5), pp. 688 - 701, 2010 [citation][year=2010]J. Bader, E. Zitzler, HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization, Evolutionary Computation, Vol. 19, No. 1, Pages 45-76, 2010 [citation][year=2010]K. Bringmann, T. Friedrich, Approximating the volume of unions and intersections of high-dimensional geometric objects, Computational Geometry Volume 43, Issues 6-7, Pages 601-610, 2010 [citation][year=2010]A. Auger, J. Bader, D. Brockhoff, Theoretically investigating optimal µ-distributions for the hypervolume indicator: first results for three objectives, Proceedings of the 11th international conference on Parallel problem solving from nature,volume 6238 of LNCS, pages 586–596. Springer, 2010. [citation][year=2010]J. Bader and E. Zitzler. A Hypervolume-Based Optimizer for High-Dimensional Objective Spaces, In Multiple Objective and Goal Programming (MOPGP 2008), volume 638 of Lecture Notes in Economics and Mathematical Systems, pages 35-54. Springer, 2010 [citation][year=2010]K. Bringmann, T. Friedrich, An Efficient Algorithm for Computing Hypervolume Contributions, Evolutionary Computation, Vol. 18, No. 3, Pages 383-402, 2010 [citation][year=2010]H. Ishibuchi, N. Tsukamoto, Y. Sakane, Y. Nojima, Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions, Proceedings of the 12th annual conference on Genetic and evolutionary computation,527-534, 2010 [citation][year=2010]D. Brockhoff, Optimal μ-Distributions for the Hypervolume Indicator for Problems with Linear Bi-objective Fronts: Exact and Exhaustive Results, Proc. Simulated Evolution and Learning, LNCS 6457, 23-34, 2010 [citation][year=2010]Edgar Reehuis, Multiobjective Optimization of Water Distribution Networks, MSc thesis, Technical Report 2010-04, Universiteit Leiden, Opleiding Informatica, 2008 [citation][year=2009]N Beume, S-metric calculation by considering dominated hypervolume as klee's measure problem, Evolutionary Computation, 2009 [citation][year=2009]N Beume, B Naujoks, M Preuss, G Rudolph, Effects of 1-Greedy\ S-Metric-Selection on Innumerably Large Pareto Fronts, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science Volume 5467, 2009, pp 21-35 [citation][year=2009]A. Auger, J. Bader, D. Brockhoff, and E. Zitzler, Theory of the Hypervolume Indicator: Optimal μ-Distributions and the Choice of the Reference Point, In Foundations of Genetic Algorithms (FOGA 2009), pages 87–102, New York, NY, USA, 2009. ACM. [citation][year=2009]K. Bringmann, T. Friedrich, Approximating the Least Hypervolume Contributor: NP-Hard in General, But Fast in Practice, Proc. of the 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), Vol. 5467 of LNCS, pages 6-20, Springer-Verlag, 2009 [citation][year=2009] K. Bringmann and T. Friedrich. Don't be greedy when calculating hypervolume contributions,Proceedings of the 10th Foundations of Genetic Algorithms Workshop (FOGA X), Orlando, Florida, pp. 103-112, 2009 [citation][year=2009]D Brockhoff, Theoretical Aspects of Evolutionary Multiobjective Optimization---A Review, INRIA technical report 7030, 2009 [citation][year=2009]T. Lust,: New metaheuristics for solving MOCO problems: application to the knapsack, traveling salesman problems and IMRT optimization. PhD Thesis, University of Mons, Belgium, 2009. [citation][year=2009]C.A.C.Coello, A Tutorial on multiobjective optimization using metaheuristics. Monografías del Seminario Matemático García de Galdeano, 1–20, 2009 [citation][year=2008]K Bringmann, T Friedrich, Approximating the volume of unions and intersections of high-dimensional geometric objects, Algorithms and Computation, 2008 [citation][year=2008]E Reehuis, N Bohrweg, Opleiding Informatica, Publication/NA, 2008 [citation][year=1999]CAC Coello, List of references on evolutionary multiobjective optimization, Laboratorio Nacional de Informática Avanzada, …, 1999 Conference Articles 2018(2 publications) [publication]S. Rebelo and Fonseca, C.M. and Bicker, J. and Penousal Machado , "Evolutionary Experiments in the Development of Typographical Posters", in 6th Conference on Computation, Communication, Aesthetics & X (xCoAx 2018), 2018 [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 2017(2 publications) [publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. and Castelli, M. , "Unsure When to Stop? Ask Your Semantic Neighbors", in Genetic and Evolutionary Computation Conference, 2017 [publication]Yang, K. and Emmerich, M.T.M. and Deutz, A. and Fonseca, C.M. , "Computing 3-D expected hypervolume improvement and related integrals in asymptotically optimal time", in Evolutionary Multi-Criterion Optimization, 9th International Conference, EMO 2017. Proceedings, LNCS 10173, 2017 2016(3 publications) [publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. and Castelli, M. , "Arbitrarily Close Alignments in the Error Space: A Geometric Semantic Genetic Programming Approach", in Genetic and Evolutionary Computation Conference, 2016 [publication]Andreia P. Guerreiro and Fonseca, C.M. , "Hypervolume Sharpe-ratio indicator: Formalization and first theoretical results", in Parallel Problem Solving from Nature - PPSN XIV, vol. 9921 of Lecture Notes in Computer Science, pp. 814-823, Springer, 2016 [publication]Correa, C.R. and Wanner, E.F. and Fonseca, C.M. , "Lyapunov design of a simple step-size adaptation strategy based on success", in Parallel Problem Solving from Nature - PPSN XIV, vol. 9921 of Lecture Notes in Computer Science, pp. 101-110, Springer, 2016 2015(4 publications) [publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. , "On the Generalization Ability of Geometric Semantic Genetic Programming", in 18th European Conference on Genetic Programming (EuroGP 2015), 2015 [citation][year=2015]Dick, Grant, Aysha P. Rimoni, and Peter A. Whigham. "A Re-Examination of the Use of Genetic Programming on the Oral Bioavailability Problem." Proceedings of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015. [citation][year=2015]Graff, Mario, Eric Sadit Tellez, Elio Villasenor, and Sabino Miranda-Jiménez. "Semantic Genetic Programming Operators Based on Projections in the Phenotype Space." [publication]Andreia P. Guerreiro and Fonseca, C.M. and Paquete, L. , " Greedy hypervolume subset selection in the three-objective case ", in Proceedings of the 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), pp 671-678 , 2015 [citation][year=2015]T Friedrich, F Neumann Maximizing submodular functions under matroid constraints by evolutionary algorithms Evolutionary computation, 2015 [publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. , "Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming", in 17th Portuguese Conference on Artificial Intelligence (EPIA 2015), 2015 [publication]Alexandre, R.F. and Campelo, F. and Fonseca, C.M. and Vasconcelos, J.A.d. , "A comparative study of algorithms for solving the multiobjective open-pit mining operational planning problems", in Evolutionary Multi-Criterion Optimization. 8th International Conference, EMO 2015. Proceedings, Part II, LNCS 9019, 2015 2014(1 publication) [publication]Yevseyeva, I. and Andreia P. Guerreiro and Emmerich, M.T.M. and Fonseca, C.M. , "A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms", in Parallel Problem Solving from Nature – PPSN XIII, 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, LNCS 8672, 2014 [citation][year=2016]Ahmadi, Aras. "Memory-based Adaptive Partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms." Applied Soft Computing (2016). [citation][year=2015]Souravlias, D., K. E. Parsopoulos, and I. S. Kotsireas. "Circulant weighing matrices: a demanding challenge for parallel optimization metaheuristics." Optimization Letters (2015): 1-12. 2012(2 publications) [publication]Grunert da Fonseca, Viviane and Fonseca, C.M. , "The relationship between the covered fraction, completeness and hypervolume indicators", in Artificial Evolution, 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers, LNCS 7401, 2012 [citation][year=2015]Michael T.M. Emmerich, André H. Deutz, Iryna Yevseyeva, "A Bayesian Approach to Portfolio Selection in Multicriteria Group Decision Making," Procedia Computer Science, volume 64, pages 993–1000, 2015. [citation][year=2013]I. Hupkens and M. Emmerich , “Logarithmic-time updates in SMS-EMOA and hypervolume-based archiving,” in EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV ( M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, E. Tantar, A.-A. Tantar, P. Del Moral, P. Legrand, P. Bouvry, C. A. Coello, eds.), vol. 227 of Advances in Intelligent Systems and Computing, pp 155-169 Berlin: Springer, 2013. [citation][year=2013]K. Narukawa, “Effect of dominance balance in many-objective optimization,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp. 276-290, Berlin: Springer, 2013. [publication]Andreia P. Guerreiro and Fonseca, C.M. and Emmerich, M.T.M. , "A fast dimension-sweep algorithm for the hypervolume indicator in four dimensions", in 24th Canadian Conference on Computational Geometry (CCCG 2012), 2012 [citation][year=2015]Yang, Zhiwei, et al. "Multicriteria Inventory Routing by Cooperative Swarms and Evolutionary Algorithms." Bioinspired Computation in Artificial Systems. Springer International Publishing, 2015. 127-137. [citation][year=2015]Siwei Jiang; Jie Zhang; Yew-Soon Ong; Zhang, A.N.; Puay Siew Tan, "A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm," in Cybernetics, IEEE Transactions on , vol.45, no.10, pp.2202-2213, Oct. 2015 doi: 10.1109/TCYB.2014.2367526 [citation][year=2014]Russo, L.M.S.and; Francisco, A.P., "Quick Hypervolume," IEEE Transactions on Evolutionary Computation, Volume 18, Issue 4, pp. 481 - 502, 2014. http://dx.doi.org/10.1109/TEVC.2013.2281525 [citation][year=2014]M. Emmerich and A. Deutz, “Time complexity and zeros of the hypervolume indicator gradient field,” in EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III (O. Schuetze, C. A. Coello Coello, A.-A. Tantar, E. Tantar, P. Bouvry, P. Del Moral, P. Legrand, eds.), vol. 500 of Studies in Computational Intelligence, pp 169-193, Berlin: Springer, 2014. [citation][year=2014]Siwei Jiang, Jie Zhang, Yew-Soon Ong, Allan N. Zhang, and Puay Siew Tan, "A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm", IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2014.2367526, Published online 2 December 2014. [citation][year=2014]Michael T.M. Emmerich, André H. Deutz, Iryna Yevseyeva, "On Reference Point Free Weighted Hypervolume Indicators based on Desirability Functions and their Probabilistic Interpretation", Procedia Technology, Volume 16, Pages 532–541, 2014. [citation][year=2014]Krzysztof Nowak, Marcus Märtens, Dario Izzo, "Empirical Performance of the Approximation of the Least Hypervolume Contributor", Parallel Problem Solving from Nature – PPSN XIII, Lecture Notes in Computer Science Volume 8672, pp 662-671, 2014. [citation][year=2013]K. Bringmann, “Bringing Order to Special Cases of Klee’s Measure Problem,” in Mathematical Foundations of Computer Science 2013 (K. Chatterjee and J. Sgall, eds.), vol. 8087 of Lecture Notes in Computer Science, pp 207-218, Berlin: Springer, 2013. [citation][year=2013]I. Hupkens and M. Emmerich , “Logarithmic-time updates in SMS-EMOA and hypervolume-based archiving,” in EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV ( M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, E. Tantar, A.-A. Tantar, P. Del Moral, P. Legrand, P. Bouvry, C. A. Coello, eds.), vol. 227 of Advances in Intelligent Systems and Computing, pp 155-169 Berlin: Springer, 2013. [citation][year=2013]A. Zhou, Q. Zhang, and G. Zhang, “Approximation Model Guided Selection for Evolutionary Multiobjective Optimization,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp 398-412, Berlin: Springer, 2013. [citation][year=2013]M. Emmerich, A. Deutz, J. Kruisselbrink, and P. K. Shukla , “Cone-Based Hypervolume Indicators: Construction, Properties, and Efficient Computation,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp 111-127, Berlin: Springer, 2013. [citation][year=2013]P. K. Shukla and M. A. Braun, “Indicator Based Search in Variable Orderings: Theory and Algorithms,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp 66-80, Berlin: Springer, 2013. [citation][year=2013]João A. Duro, Machine learning based decision support for a class of many-objective optimisation problems, Ph.D. Thesis, Cranfield University, 2013. 2011(1 publication) [publication]Fonseca, C.M. and Andreia P. Guerreiro and López-Ibáñez, M. and Paquete, L. , "On the Computation of the Empirical Attainment Function", in Sixth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2011), LNCS 6576, 106-190, Springer, 2011 [citation][year=2015]JP Martins, Análise da aprendizagem de ligações em otimização evolutiva, Tese de Doutorado, Instituto de Ciências Matemáticas e de Computação, USP São Carlos, 2015. [citation][year=2015]Ioannis Tsoukalas, Christos Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling & Software, Volume 69, July 2015, Pages 396-413, ISSN 1364-8152, http://dx.doi.org/10.1016/j.envsoft.2014.09.023. [citation][year=2015]WP Tsai, FJ Chang, LC Chang, EE Herricks AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands Journal of Hydrology, 2015 [citation][year=2015]L Yliniemi, D Wilson, K Tumer Multi-Objective Multiagent Credit Assignment in NSGA-II Using Difference Evaluations AAMAS '15 Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems Pages 1635-1636 [citation][year=2015]LM Yliniemi Multi-Objective Optimization in Multiagent Systems PhD thesis, Oregon State University [citation][year=2014]T Tušar, B Filipic, Initial experiments in visualization of empirical attainment function differences using maximum intensity projection,Proceedings of the 2014 conference on Genetic and evolutionary computation companion, Pages 1099-1106, 2014 [citation][year=2014]I Tsoukalas, C Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling & Software, 2014 [citation][year=2014]T Tušar, B Filipic, Visualizing Exact and Approximated 3D Empirical Attainment Functions, Mathematical Problems in Engineering, 2014 [citation][year=2014]L Yliniemi, K Tumer, PaCcET: An Objective Space Transformation to Iteratively Convexify the Pareto Front , Simulated Evolution and Learning, 2014 [citation][year=2014]FMO no Guzmán, IR Ruiz, Development of Advanced Computational Systems for Multiple Sequence Alignments by using Heterogeneous Biological Information, Doctoral Thesis - Francisco M Ortuno Guzman, 2014 [citation][year=2013]T. Tušar and B. Filipic, "An approach to visualizing the 3D empirical attainment function," in GECCO '13 Companion Proceedings, Fifteenth Annual Conference on Genetic and Evolutionary Computation (C. Blum, ed.), pp. 1367-1372, 2013. [citation][year=2012]Michael P. Cipold, Pradyumn Kumar Shukla, Claus C. Bachmann, Kaibin Bao, Hartmut Schmeck , An Evolutionary Optimization Approach for Bulk Material Blending Systems, Parallel Problem Solving from Nature - PPSN XII [citation][year=2012]D Brockhoff, K Deb, GECCO 2012 tutorial on evolutionary multiobjective optimization, Proceedings of the fourteenth international conference on Genetic and evolutionary computation Edited Books 2018(2 publications) [publication]Auger, A. and Fonseca, C.M. and Lourenço, Nuno and Penousal Machado and Paquete, L. and Whitley, D. , "Proceedings of the 15th International Conference on Parallel Problem Solving from Nature – (PPSN XV) - Part 1", vol. 11101, 2018 [publication]Auger, A. and Fonseca, C.M. and Lourenço, Nuno and Penousal Machado and Paquete, L. and Whitley, D. , "Proceedings of the 15th International Conference on Parallel Problem Solving from Nature – (PPSN XV) - Part 2", vol. 11102, 2018 2013(1 publication) [publication]Purshouse, R.C. and Fleming, P.J. and Fonseca, C.M. and Greco, S. and Shaw, J. ,Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 , vol. 7811, 2013 Book Chapters 2016(1 publication) [publication]Emmerich, M.T.M. and Yang, K. and Deutz, A. and Wang, H. and Fonseca, C.M. , "A multicriteria generalization of Bayesian global optimization", in Advances in Stochastic and Deterministic Global Optimization, vol. 107, pp. 229-242, 2016 Tech Report 2017(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. , "Computing and updating hypervolume contributions in up to four dimensions", 2017 2015(2 publications) [publication]Lacour, R. and Klamroth, K. and Fonseca, C.M. , "A Box Decomposition Algorithm to Compute the Hypervolume Indicator", 2015 [publication]Figueira, J. and Fonseca, C.M. and Halffmann, P. and Klamroth, K. and Paquete, L. and Ruzika, S. and Schulze, B. and Stiglmayr, M. and Willems, D. , "Easy to say they’re hard, but hard to see they’re easy: Toward a categorization of tractable multiobjective combinatorial optimization problems", 2015 2014(2 publications) [publication]Vaz, D. and Paquete, L. and Fonseca, C.M. and Klamroth, K. and Stiglmayr, M. , "Representation of the non-dominated set in biobjective combinatorial optimization", 2014 [publication]Kuhn, T. and Fonseca, C.M. and Paquete, L. and Ruzika, S. and Figueira, J. , "Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms", 2014 [citation][year=2014]Karl Bringmann, Tobias Friedrich, Patrick Klitzke, Two-dimensional subset selection for hypervolume and epsilon-indicator, Proceedings of the 2014 conference on Genetic and evolutionary computation Pages 589-596 2008(1 publication) [publication]Beume, N. and Fonseca, C.M. and López-Ibáñez, M. and Paquete, L. and Vahrenhold, J. , "On the complexity of computing the hypervolume indicator", 2008 [citation][year=2009]-H. Trautman, T.Wagner, B. Naujoks, M. Press, J. Mehnen, Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms, Vol. 17, No. 4, Pages 493-509. [citation][year=2009]- N. Beume, S-Metric Calculation by Considering Dominated Hypervolume as Klees Measure Problem, Evolutionary Computation, Vol. 17, No. 4, Pages 477-492, 2009. [citation][year=2009]- Dimo Brockhoff, Theoretical Aspects of Evolutionary Multiobjective Optimization, Technical Report 7030, INRIA, 2009. [citation][year=2009]- N. Beume, B. Naujoks, M. Preuss, G. Rudolph, T. Wagner, Effects of 1-Greedy S-Metric-Selection on Innumerably Large Pareto Fronts, EMO 2009, LNCS 5369, Springer, pp. 21-35, 2009. [citation][year=2009]- Karl Bringmann and Tobias Friedrich, Approximating the least hypervolume contributor: NP-hard in general, but fast in practice, EMO 2009, LNCS 5369, Springer, pp. 6-20, 2009 [citation][year=2009]- Karl Bringmann and Tobias Friedrich, Don t be greedy when calculating hypervolume contributions, ACM Foundations of Genetic Algorithms - FOGA 2009. [citation][year=2009]- A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Theory of the Hypervolume Indicator: Optimal mu-Distributions and the Choice of the Reference Point, ACM Foundations of Genetic Algorithms - FOGA 2009. [citation][year=2008]- Edgar Reehuis, Multiobjective Optimization of Water Distribution Networks Using SMS-EMOA, Technical report 08-12, Universiteit Leiden, Opleiding Informatica, 2008 [citation][year=2008]- Karl Bringmann and Tobias Friedrich, Approximating the Volume of Unions and Intersections of High-Dimensional Geometric Objects, LNCS 5369, Springer, pp. 436-447, 2008 [citation][year=2008]- J. Bader and E. Zitzler. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization. TIK Report 286, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, November 2008.