2009•Unpublished venueRequires access

The effect of crossover on evolution ability of population

Qingwu Fan, Pu Wang, Jing Huang

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Abstract

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, this paper presents some important conclusions. The important method to improve 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.

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What this paper is about

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, this paper presents some important conclusions. The important method to improve 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.

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Available abstract

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, this paper presents some important conclusions. The important method to improve 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.

Key concepts: Crossover, Population, Premature convergence, Selection (genetic algorithm), Convergence (economics), Mutation, Computer science, Mathematical optimization

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