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http://dx.doi.org/10.5391/JKIIS.2009.19.1.062

New Generation Gap Models for Evolutionary Algorithm in Real Parameter Optimization  

Choi, Jun-Seok (서경대학교 전자공학과)
Seo, Ki-Sung (서경대학교 전자공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.1, 2009 , pp. 62-68 More about this Journal
Abstract
Two new generation gap models with modified parent-centric recombination(PCX) operator are proposed. First, the self-adaptation generation gap(SGG) model is a control method that keeps a replaced probability of parents by offspring to a certain level which obtains better performance. Second, virtual cluster generation gap(VCGG) is provided to extend distances among parents using clustering, which causes it to diversify individuals. In this model, distances among parents can be controlled by size of clusters. To demonstrate the effectiveness of our two proposed approaches, experiments for three standard test problems are executed and compared to most competing current approaches, CMA-ES and Generalized Generation Gap(G3) with PCX. It is shown two proposed methods are superior to consistently other approaches in the study.
Keywords
Generation Gap Model; Real-parameter Optimization; Evolutionary Algorithm; SGG; VCGG;
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