A hybrid of adaptive neuro-fuzzy inference system and genetic algorithm

Article type: Research Article

Authors: Varnamkhasti, M. Jalali

Affiliations: School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, DE, Malaysia

Note: [] Corresponding author. M. Jalali Varnamkhasti, School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, DE, Malaysia. Tel.: +98 93 7486 2815; E-mail: [email protected]

Abstract: Premature convergence is an important problem in evolutionary algorithms, in particular genetic algorithm. The diversity of the population is a very influence paprameter on premature convergence in genetic algorithm. In this paper, we attempt to improve the performance of genetic algorithms by providing a bi-linear allocation lifetime approach to label the chromosomes based on their fitness values. These labales applied within a set of fuzzy rules and adaptive neuro-fuzzy inference system genetic algorithm to select suitable sexual chromosomes for recombination. We have evaluated the proposed technique on several numerical functions by comparing its performance to the basic genetic algorithm. The results of our initial experiments demonstrate a clear advantage of the adaptive neuro-fuzzy inference system genetic algorithm over the other techniques.

Keywords: Adaptive neuro-fuzzy inference system, genetic algorithm, sexual selection

DOI: 10.3233/IFS-120685

Journal: Journal of Intelligent & Fuzzy Systems, vol. 25, no. 3, pp. 793-796, 2013