Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The measure of Pareto optima applications to multi-objective metaheuristics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An evolutionary multiobjective approach to design highly non-linear Boolean functions
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Improving NSGA-II Algorithm Based on Minimum Spanning Tree
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Robust Optimization by ε -Ranking on High Dimensional Objective Spaces
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms
IEICE - Transactions on Information and Systems
Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
International Journal of Hybrid Intelligent Systems
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This work focuses on the working principles, behavior, and performance of state of the art multiobjective evolutionary algorithms (MOEAs) on discrete search spaces by using MNK-Landscapes. Its motivation comes from the performance shown by NSGA-II and SPEA2 on epistatic problems, which suggest that simpler population-based multiobjective random one-bit climbers are by far superior. Adaptive evolution is a search process driven by selection, drift, mutation, and recombination over fitness landscapes. We group MOEAs features and organize our study around these four important and intertwined processes in order to understand better their effects and clarify the reasons to the poor performance shown by NSGA-II and SPEA2. This work also constitutes a valuable guide for the practitioner on how to set up its algorithm and gives useful insights on how to design more robust and efficient MOEAs.