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Journal of Zhejiang University SCIENCE A 2002 Vol.3 No.4 P.431~434

http://doi.org/10.1631/jzus.2002.0431


Improved genetic operator for genetic algorithm


Author(s):  LIN Feng, YANG Qi-wen

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   eeflin@zjuem.zju.edu.cn

Key Words:  Genetic algorithm (GA), Mutation operator, Premature convergence


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LIN Feng, YANG Qi-wen. Improved genetic operator for genetic algorithm[J]. Journal of Zhejiang University Science A, 2002, 3(4): 431~434.

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author="LIN Feng, YANG Qi-wen",
journal="Journal of Zhejiang University Science A",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2002.0431"
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T1 - Improved genetic operator for genetic algorithm
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2002.0431


Abstract: 
The mutation operator has been seldom improved because researchers hardly suspect its ability to prevent genetic algorithm (GA) from converging prematurely. Due to its importance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some extent, can result in premature convergence of genes (PCG) in the same locus. The a bove drawback of the traditional mutation operator causes the loss of critical alleles. Inspired by digital technique, we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function optimization show that the improved mutation operator can effectively prevent premature convergence, and can provide a wide selection range of control parameters for GA.

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Reference

[1] Back, T., Hoffmeister, F., 1991. Extended selection mechanisms in genetic algorithms, Proc. 4th Int. Conf. Genetic Algorithms, Univ of California, Sandiego, CA, p.14-21.

[2] Eshelman, L. J., Shaffer, J. D., 1991. Preventing premature convergence in Genetic algorithms by preventing incest, Proc. 4th Int. Conf. Genetic Algorithms, p.115-122.

[3] Grefenstette, J. J., 1986. Optimization of control parameters for genetic algorithms, IEEE Trans. Syst., Man, Cybren., 16(1): 122-128.

[4] Grefenstette, J. J., Gopal, R. R., and Van Gucht, D., 1985. Genetic Algorithms for the Traveling Salesman Problem, Proc. Int. Conf. Genetic Algorithms and Their Applications, p.160-168.

[5] Holland, J. H., 1975. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michigan Press.

[6] Lin Feng, 2001. New sensorless speed vector control of induction motor drive system, Journal of Zhejiang University (Engineering Science), 35(1):67-71(in Chinese, with English abstract).

[7] Miller, J. A., Potter, W. D., Gandham R. V., 1993. An evaluation of local improvement operators for genetic algorithms. IEEE Trans. Syst., Man, Cybern., 23(5):1340-1350.

[8] Potts, J. C., Giddens, T. D. and Yadev, S. B., 1994. The Development and evaluation of an improved genetic algorithm based on migration and artificial selection, IEEE Trans. Syst., Man, Cybern., 24(1): 73-85.

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