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Journal of Information Science and Engineering, Vol.19 No.5, pp.889-903 (September 2003)


Adapting Crossover and Mutation Rates in Genetic Algorithms*

Wen-Yang Lin, Wen-Yung Lee+ and Tzung-Pei Hong**
Department of Information Management
I-Shou University
Kaohsiung, 840 Taiwan
E-mail: wyling@isu.edu.tw
+R&D Department
TransAsia Telecommunications Inc.
Kaohsiung, 806 Taiwan
E-mail: anttyl@tat.com.tw
**Department of Electrical Engineering
National University of Kaohsiung
Kaohsiung, 811 Taiwan
E-mail: tphong@nuk.edu.tw

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.

Keywords: genetic algorithms, self-adaptation, progressive value, crossover rate, mutation rate

Full Text () Retrieve PDF document (200309_09.pdf)

Received January 11, 2002; revised August 26, 2002; accepted January 24, 2003.
Communicated by Hsu-Chun Yen.
*A preliminary version of this paper was presented at the Sixth Conference on Artiticial Intelligence and Applications, Kaohsiung, Taiwan, in Novmber 9, 2001, sponsored by the Taiwanese Association for Artificial Intelligence.