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IEICE Transactions on Information and Systems 2008 E91-D(4):1206-1210; doi:10.1093/ietisy/e91-d.4.1206
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Copyright © 2008 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Letters -- Artificial Intelligence and Cognitive Science

{delta}-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms

Hernán AGUIRRE1, Masahiko SATO1 and Kiyoshi TANAKA1

1 The authors are with the Faculty of Engineering, Shinshu University, Nagano-shi, 380–8553 Japan. E-mail: ahernan{at}shinshu-u.ac.jp


  Abstract

In this paper, we propose {delta}-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.

Key Words: multiobjective evolutionary algorithms, {delta}-similar elimination, controlled elitism, selection

Manuscript received August 21, 2007.


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