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Selective Mutation for Genetic Algorithms

Authors: Sung Hoon Jung


In this paper, we propose a selective mutation method for improving the performances of genetic algorithms. In selective mutation, individuals are first ranked and then additionally mutated one bit in a part of their strings which is selected corresponding to their ranks. This selective mutation helps genetic algorithms to fast approach the global optimum and to quickly escape local optima. This results in increasing the performances of genetic algorithms. We measured the effects of selective mutation with four function optimization problems. It was found from extensive experiments that the selective mutation can significantly enhance the performances of genetic algorithms.

Keywords: Genetic algorithm, selective mutation, function optimization

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[1] D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989.
[2] M. Srinivas and L. M. Patnaik, "Genetic Algorithms: A Survey," IEEE Computer Magazine, pp. 17-26, June 1994.
[3] J. L. R. Filho and P. C. Treleaven, "Genetic-Algorithm Programming Environments," IEEE Computer Magazine, pp. 28-43, June 1994.
[4] D. Beasley, D. R. Bull, and R. R. Martin, "An Overview of Genetic Algorithms: Part 1, Fundamentals," Technical Report obtained fromˆ jimtoer/GA Overview1.pdf.
[5] D. B. Fogel, "An Introduction to Simulated Evolutionary Optimization," IEEE Transactions on Neural Networks, vol. 5, pp. 3-14, Jan. 1994.
[6] H. Szczerbicka and M. Becker, "Genetic Algorithms: A Tool for Modelling, Simulation, and Optimization of Complex Systems," Cybernetics and Systems: An International Journal, vol. 29, pp. 639-659, Aug. 1998.
[7] R. Yang and I. Douglas, "Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique," Journal of Optimization Theory and Applications, vol. 98, pp. 449-465, Aug. 1998.
[8] C. Xudong, Q. Jingen, N. Guangzheng, Y. Shiyou, and Z. Mingliu, "An Improved Genetic Algorithm for Global Optimization of Electromagnetic Problems," IEEE Transactions on Magnetics, vol. 37, pp. 3579- 3583, Sept. 2001.
[9] J. A. Vasconcelos, J. A. Ramirez, R. H. C. Takahashi, and R. R. Saldanha, "Improvements in Genetic Algorithms," IEEE Transactions on Magnetics, vol. 37, pp. 3414-3417, Sept. 2001.
[10] E. Alba and B. Dorronsoro, "The exploration/exploitation tradeoff in dynamic cellular genetic algorithms," IEEE Transactions on Evolutionary Computation, vol. 9, pp. 126-142, Apr. 2005.
[11] V. K. Koumousis and C. Katsaras, "A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance," IEEE Transactions on Evolutionary Computation, vol. 10, pp. 19-28, Feb. 2006.
[12] J. Andre, P. Siarry, and T. Dognon, "An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization," Advances in engineering software, vol. 32, no. 1, pp. 49-60, 2001.
[13] J. E. Smith and T. C. Fogarty, "Operator and parameter adaptation in genetic algorithms," Soft computing : a fusion of foundations, methodologies and applications, vol. 92, no. 2, pp. 81-87, 1997.
[14] C. W. Ho, K. H. Lee, and K. S. Leung, "A Genetic Algorithm Based on Mutation and Crossover with Adaptive Probabilities," in Proceedings of the 1999 Congress on Evolutionary Computation, vol. 1, pp. 768-775, 1999.
[15] S. H. Jung, "Queen-bee evolution for genetic algorithms," Electronics Letters, vol. 39, pp. 575-576, Mar. 2003.
[16] K. DeJong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan, 1975.