δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms

Hernan AGUIRRE  Masahiko SATO  Kiyoshi TANAKA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.4   pp.1206-1210
Publication Date: 2008/04/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Artificial Intelligence and Cognitive Science
Keyword: 
multiobjective evolutionary algorithms,  δ-similar elimination,  controlled elitism,  selection,  

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Summary: 
In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.