Convergence Analysis on Gene Expression Programming

摘要：

The gene expression system works with mutation, inversion, transposition, recombination as well as fitness-proportional selection with elitism. As a novel geno-type/phenotype evolutionary algorithm, gene expression programming has many applications, but little is know about its convergence properties. This article deal with the convergence of two kinds of gene expression programming algorithms. By analytical techniques of homogeneous finite Markov chains, we perform proofs that the canonical gene expression algorithms could not converge to the global optimum with probability 1, while the modified algorithm do.

关键词： Gene expression programming Markov chain Convergence

作者: Ming Chen Lixin Ding Jianping Yu

作者单位: Slate Key Laboratory of Software Engineering Wuhan University,Wuhan 430072,China College of Mathemat State Key Laboratory of Software Engineering Wuhan University Wuhan 430072,China College of Mathematics and Computer Science Hunan Normal University Changsha 410081,China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 45-49

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）