Abstract
In this paper, we propose a GA with a new crossover method appropriate for real value chromosomes, called the ”Unfair Average Crossover”, an automatic fuzzy rule extraction method that uses our GA and a real value chromosome coding method in which parameters in membership functions of fuzzy if-then rules are directly represented. It is shown that our method is superior to conventional methods using discrete chromosome coding in cases where there is a tendency for data to change dynamically.
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© 1996 Springer-Verlag Berlin Heidelberg
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Nomura, T., Miyoshi, T. (1996). Numerical coding and unfair average crossover in GA for fuzzy rule extraction in dynamic environments. In: Furuhashi, T., Uchikawa, Y. (eds) Fuzzy Logic, Neural Networks, and Evolutionary Computation. WWW 1995. Lecture Notes in Computer Science, vol 1152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61988-7_16
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DOI: https://doi.org/10.1007/3-540-61988-7_16
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