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EMO
2006
Springer

Differential Evolution versus Genetic Algorithms in Multiobjective Optimization

9 years 11 months ago
Differential Evolution versus Genetic Algorithms in Multiobjective Optimization
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 multiobjective 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.
Tea Tusar, Bogdan Filipic
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where EMO
Authors Tea Tusar, Bogdan Filipic
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