Abstract
This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples.
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References
G. Rudolph, Convergence analysis of canonical genetic algorithms. IEEE Trans. on NN, NN-5(1994)1, 96–101.
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Learning. Addison Wesley Publishing Company, 1989, 40.
D. J. Janson, J. F. Frenzel, Training product unit neural networks with genetic algorithms, IEEE Expert, 8(1993)5, 26–33.
I. Yasumasa, K. Hiroaki, Application of an improved genetic algorithm to the learning of neural networks, IEICE Trans. Fundamentals, 1994, E77-A(1994)4, 731–735.
N. Tomoharu, A. Takeshi, N. Hiroshi, Structural evolution of neural networks having arbitrary connections by a genetic method, IEICE Trans. Information & System, E76-D(1993),6, 689–697.
L. J. Zhang, Z. H. Mao, Y. D. Li, An, improved genetic algorithm based on combinative theory & fuzzy reasoning and its applications. International Conference on Neural Information Processing, Korea, 1994, 180–185.
J. J. Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Trans. on SMC, SMC-16(1986)1, 122–128.
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Supported by the Climbing Program—National Key Project for Fundamental Research in China, Grant NSC92097
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Liangjie, Z., Zhihong, M. & Yanda, L. Mathematical analysis of mutation operator and its improved strategy in genetic algorithms. J. of Electron.(China) 14, 154–158 (1997). https://doi.org/10.1007/s11767-997-1008-2
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DOI: https://doi.org/10.1007/s11767-997-1008-2