Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Hybrid Method for Improvement of Evolutionary Computation

진화 연산의 성능 개선을 위한 하이브리드 방법

  • 정진기 (포항공과대학교 전자컴퓨터공학부) ;
  • 오세영 (포항공과대학교 전자컴퓨터공학부)
  • Published : 2002.08.01
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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.

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References

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