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

Authors:
Hernán Aguirre;Masahiko Sato;Kiyoshi Tanaka
Affiliations:
-;-;-
Venue:
IEICE - Transactions on Information and Systems
Year:
2008

Citing 2
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Selection, drift, recombination, and mutation in multiobjective evolutionary algorithms on scalable MNK-Landscapes

EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization

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Abstract

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.