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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/39988

    Title: Modulus genetic algorithm and its appliction to fuzzy system optimization
    Other Titles: 模數遺傳演算法及其在模糊系統最佳化之應用
    Authors: Lin, Sinn-cheng
    Contributors: 淡江大學資訊與圖書館學系
    Date: 1999-07
    Issue Date: 2010-01-27 16:55:29 (UTC+8)
    Publisher: University of British Columbia
    Abstract: The conventional genetic algorithm encodes the searched parameters as binary strings. After applying the basic genetic operators such as reproduction, crossover and mutation, a decoding procedure is used to convert the binary strings to the original parameter space. As the result, such an encoding/decoding procedure leads to considerable numeric errors. This paper proposes a new algorithm called modulus genetic algorithm (MGA) that uses the modulus operation to resolve this problem. In the MGA, the encoding/decoding procedure is not necessary. It has the following advantages: 1) the evolution can be speeded up; 2) the numeric truncation error can be avoided; 3) the precision of solution can be increased. The proposed MGA is applied to resolve the key problem of fuzzy inference systems-rule acquisition. The fuzzy system with MGA as learning mechanism forms an ?ntelligent fuzzy system?? Based on the proposed approach, the fuzzy rule base can be self-extracted and optimized
    Relation: Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on vol.1, pp.669-674
    DOI: 10.1109/IPMM.1999.792573
    Appears in Collections:[Graduate Institute & Department of Information and Library Sciences] Proceeding

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