Evolution of appropriate crossover and mutation operators in a genetic process

被引：67

作者：

Hong T.-P. ^{[1
]}

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

Lin W.-Y. ^{[1
]}

Lee W.-Y. ^{[3
]}

机构：

[1] Department of Information Management, I-Shou University

[2] Institute of Electrical Engineering, Chung-Hua University

[3] Institute of Information Engineering, I-Shou University

来源：

Applied Intelligence | 2002年 / 16卷 / 1期

关键词：

Crossover; Evolution; Genetic algorithms; Mutation;

D O I：

10.1023/A:1012815625611

中图分类号：

学科分类号：

摘要：

Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.

引用

页码：7 / 17

页数：10

共 37 条

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