Study on Genetic Algorithm Based on Schema Mutation and Its Performance Analysis
Genetic algorithm (GA), as a kind of important intelligence computing tool, is a wide research content in the academic circle and the application domain now. In this paper, for the mutation operation of GA, by combining with the essential feature, we establish a genetic algorithm based on schema mutation (denoted by SM-GA, for short). Further, we discuss the global convergence of CM-GA by using the Markov chain theory, and analyze the performance of SM-GA through an example. All the results indicate that, SM-GA is higher than the ordinary binary code genetic algorithm (denoted by B2GA, for short) in convergence precision. There was no significant difference between SM-GA and B2GA in convergence time. SM-GA overcomes the problem that B2GA can not converge strongly to some extent.
genetic algorithm binary coding schema mutation Markov chain
Fachao Li Tingyu Zhang
School of Economics and Management Hebei University of Science and Technology Shijiazhuang 050018, C School of Science Hebei University of Science and Technology Shijiazhuang 050018, China
国际会议
Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)
南昌
英文
548-551
2009-05-22(万方平台首次上网日期,不代表论文的发表时间)