Abstract
This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMONS-II, DEMOSP2 and DEMOIB. Experimental results on 16 numerical multi-objective test problems show that on the majority of problems, the algorithms based on differential evolution perform significantly better than the corresponding genetic algorithms with regard to applied quality indicators. This suggests that in numerical multiobjective optimization, differential evolution explores the decision space more efficiently than genetic algorithms. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Tušar, T., & Filipič, B. (2007). Differential evolution versus genetic algorithms in multiobjective optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4403 LNCS, pp. 257–271). Springer Verlag. https://doi.org/10.1007/978-3-540-70928-2_22
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