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Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

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Abstract

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms (SGA) as the values of crossover probability (P c) and mutation probability (P m) are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm (FAGA) based on the auto-regulating fuzzy rules (ARFR-FAGA). By using the fuzzy control method, the values of P c and P m were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise (EFAGA) obviously in the global search.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Shou-yi Yu  (喻寿益) & Su-qiong Kuang  (邝溯琼)

Authors
  1. Shou-yi Yu  (喻寿益)
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  2. Su-qiong Kuang  (邝溯琼)
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Corresponding author

Correspondence to Shou-yi Yu  (喻寿益).

Additional information

Foundation item: Project(60574030) supported by the National Natural Science Foundation of China; Key Project(60634020) supported by the National Natural Science Foundation of China

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Yu, Sy., Kuang, Sq. Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules. J. Cent. South Univ. Technol. 17, 123–128 (2010). https://doi.org/10.1007/s11771-010-0020-8

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  • DOI: https://doi.org/10.1007/s11771-010-0020-8

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