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http://dx.doi.org/10.5391/JKIIS.2002.12.4.317

A Hybrid Method for Improvement of Evolutionary Computation  

Chung, Jin-Ki (포항공과대학교 전자컴퓨터공학부)
Oh, Se-Young (포항공과대학교 전자컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.12, no.4, 2002 , pp. 317-322 More about this Journal
Abstract
The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.
Keywords
evolutionary programming; crossover; mutation; tournament selection; genetic algorithms;
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