Reproduction strategy based on self-organizing map for genetic algorithms

被引：0
|
作者：
Kubota, Ryosuke
Horio, Keiichi
Yamakawa, Takeshi
机构：
[1] Kyushu Inst Technol, Grad Sch Comp Sci & Syst Engn, Fukuoka 8208502, Japan
[2] Kyushu Inst Technol, Grad Sch Life Sci & Syst Engn, Wakamatsu Ku, Fukuoka 8080196, Japan
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2005年 / 1卷 / 04期
关键词：
genetic algorithm; self-organizing map; reproduction; genetic diversity; fitness;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel reproduction strategy by employing a Self-Organizing Map (SOM) for two types of Genetic Algorithms (GAs) is proposed to maintain genetic diversity of population. In the proposed reproduction strategy, a set of new chromosomes in the next generation is decided by a learning of the SOM with modified updating equation based on fitness values. The approximation ability of the SOM facilitates the preservation of the genetic diversity. The proposed reproduction strategy can be applied to "Bit-String GA" and "Real-Coded GA" by employing the SOM with real value weight vectors and binary weight vectors, respectively.
引用
收藏
页码：595 / 607
页数：13
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