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作者:

Fan, Qingwu (Fan, Qingwu.) | Wang, Pu (Wang, Pu.) (学者:王普) | Huang, Jing (Huang, Jing.)

收录:

CPCI-S

摘要:

Currently most of the analysis about the running mechanism of GA focuses on the convergence problem while few focus on population characteristics after the single generation descendiblity. This paper presents the concept of evolution ability of population and discusses the ability of finding the optimal solution for the population after one-generation selection, crossover and mutation. Based on the analysis of effect of crossover on evolution ability of population, the experimental results show that the important method for improving evolution ability of population is to include larger crossover optimal solution area in a smaller crossover family area. If the crossover optimal solution area isn't included in any crossover family area of population, the population either converges to the optimal solution, or evolution will be trapped in the premature of convergence. These conclusions above do not only help improve the GA, but also provide the basis for later research work.

关键词:

Crossover Operators Evolution Ability of Population Genetic Algorithm

作者机构:

  • [ 1 ] [Fan, Qingwu]Beijing Univ Technol, Beijing, Peoples R China
  • [ 2 ] [Huang, Jing]Beijing Univ Technol, Beijing, Peoples R China

通讯作者信息:

  • [Fan, Qingwu]Beijing Univ Technol, 89 Lu Yuan S Ave, Beijing, Peoples R China

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来源 :

WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)

年份: 2009

页码: 113-118

语种: 英文

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