Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics

Authors: Yun Y.¹; Gen M.²

Source: Fuzzy Optimization and Decision Making, Volume 2, Number 2, June 2003 , pp. 161-175(15)

Publisher: Springer

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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.

Keywords: adaptive genetic algorithms; adaptive abilities; fuzzy logic controller

Language: English

Document Type: Research article

Affiliations: 1: School of Automotive, Industrial & Mechanical Engineering, Daegu University, Kyungbook 712-714, South Korea joy629@hitel.net 2: Graduate School of Information, Production & Systems, Waseda University, Kitakyushu 326-8558, Japan gen@ashitech.ac.jp

Publication date: 2003-06-01