Convergence in evolutionary programs with self-adaptation

被引：22

作者：

Greenwood, GW ^{[1
]}

Zhu, QJ

机构：

[1] Portland State Univ, Dept Elect & Comp Engn, Portland, OR 97207 USA

[2] Western Michigan Univ, Dept Math & Stat, Kalamazoo, MI 49008 USA

来源：

EVOLUTIONARY COMPUTATION | 2001年 / 9卷 / 02期

关键词：

convergence; evolutionary programming; evolution strategies; self-adaptation;

D O I：

10.1162/106365601750190389

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Evolutionary programs are capable of finding good solutions to difficult optimization problems. previous analysis of their convergence properties has normally assumed the strategy parameters are kept constant, although in practice these parameters are dynamically altered. In this paper, we propose a modified version of the 1/5-success rule for self-adaptation in evolution strategies (ES). Formal proofs of the long-term behavior produced by our self-adaptation method are included. Both elitist and non-elitist ES variants are analyzed. Preliminary tests indicate an ES with our modified self-adaptation method compares favorably to both a non-adapted ES and a 1/5-success rule adapted ES.

引用

页码：147 / 157

页数：11

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