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Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics

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Abstract

In this paper, we propose some genetic algorithms with adaptive abilities and compare with them. Crossover and mutation operators of genetic algorithms are used for constructing the adaptive abilities. All together four adaptive genetic algorithms are suggested: one uses a fuzzy logic controller improved in this paper and others employ several heuristics used in conventional studies. These algorithms can regulate the rates of crossover and mutation operators during their search process. All the algorithms are tested and analyzed in numerical examples. Finally, a best genetic algorithm is recommended.

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Authors and Affiliations

  1. School of Automotive, Industrial & Mechanical Engineering, Daegu University, Kyungbook, 712-714, South Korea

    Youngsu Yun

  2. Graduate School of Information, Production & Systems, Waseda University, Kitakyushu, 326-8558, Japan

    Mitsuo Gen

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  1. Youngsu Yun
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  2. Mitsuo Gen
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Yun, Y., Gen, M. Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics. Fuzzy Optimization and Decision Making 2, 161–175 (2003). https://doi.org/10.1023/A:1023499201829

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