Differentiate coevolutionary algorithm

被引：0

作者：

Li B. ^{[1
]}

Zhao X.-F. ^{[2
]}

Zhang Q.-S. ^{[1
]}

Tan S.-H. ^{[2
]}

机构：

[1] Cisco School of Informatics, Guangdong University of Foreign Studies

[2] School of Electronic and Information Engineering, South China University of Technology

来源：

Journal of Convergence Information Technology | 2011年 / 6卷 / 04期

关键词：

Coevolution; Coevolutionary algorithm; Convergence; Fitness; Genetic algorithm;

D O I：

10.4156/jcit.vol6.issue4.21

中图分类号：

学科分类号：

摘要：

This paper describes a Differentiate CoEvolutionary Algorithm (DCEA), in which each individual has not only chromosomal fitness but also survival fitness. The chromosomal fitness is determined by the individual's chromosome completely, which is the same as that in the genetic algorithm (GA). The survival fitness is determined by the individual's chromosome and the coevolutionary relationships in which it is involved, measuring the individual's survival ability. DCEA embodies all the coevolutionary relationships ubiquitous in natural ecosystems. The total influence received by an individual from all the other individuals has only three possibilities: benefit, harm or neutral. DCEA is tested on two numeric benchmark function optimization problems. Experimental results show that DCEA outperforms GA, showing faster convergence performance and obtaining a better balance between exploitation and exploration. But the value of coevolutionary factors should adjust self-adaptively according to the optimized objects or the dynamic environments.

引用

页码：180 / 187

页数：7

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