Adapting crossover and mutation rates in genetic algorithms

被引:0
作者
Lin, WY [1 ]
Lee, WY
Hong, TP
机构
[1] I Shou Univ, Dept Informat Management, Kaohsiung 840, Taiwan
[2] TransAsia Telecommun Inc, R&D Dept, Kaohsiung 806, Taiwan
[3] Natl Univ Kaohsiung, Dept Elect Engn, Kaohsiung 811, Taiwan
关键词
genetic algorithms; self-adaptation; progressive value; crossover rate; mutation rate;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
It is well known that a judicious choice of crossover and/or mutation rates is critical to the success of genetic algorithms. Most earlier researches focused on finding optimal crossover or mutation rates, which vary for different problems, and even for different stages of the genetic process in a problem. In this paper, a generic scheme for adapting the crossover and mutation probabilities is proposed. The crossover and mutation rates are adapted in response to the evaluation results of the respective offspring in the next generation. Experimental results show that the proposed scheme significantly improves the performance of genetic algorithms and outperforms previous work.
引用
收藏
页码:889 / 903
页数:15
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