Differential evolution in high-dimensional search spaces

被引：14

作者：

Olorunda, Olusegun ^{[1
]}

Engelbrecht, Andries P. ^{[1
]}

机构：

[1] Univ Pretoria, Dept Comp Sci, ZA-0002 Pretoria, South Africa

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4424710

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A possible way of dealing with a high dimensional problem space is to divide it up into smaller parts, and to have each part optimized by a separate population. A mechanism is then defined to construct a complete solution from the subpopulations, and to evaluate the entities contained in the subpopulations. This form of cooperation has been successfully applied to Particle Swarm Optimization (PSO), by [1] in the cooperative split PSO, and to genetic algorithms, in the cooperative coevolutionary genetic algorithm, developed by [2], on which the cooperative split PSO is based. This paper investigates cooperation in differential evolution (DE) with the aim of determining the effects of multiple participants in dealing with high-dimensional problem spaces.

引用

页码：1934 / 1941

页数：8

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