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

M. Jalali Varnamkhasti, Nasruddin Hassan

Research output: Contribution to journalArticle

36 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)793-796
Number of pages4
JournalJournal of Intelligent and Fuzzy Systems
Volume25
Issue number3
DOIs
Publication statusPublished - 2013

Fingerprint

Adaptive Neuro-fuzzy Inference System
Fuzzy inference
Genetic algorithms
Genetic Algorithm
Premature Convergence
Chromosomes
Chromosome
Fuzzy rules
Fuzzy Rules
Recombination
Evolutionary algorithms
Fitness
Evolutionary Algorithms
Labels
Lifetime
Demonstrate
Experiment
Experiments

Keywords

  • Adaptive neuro-fuzzy inference system
  • genetic algorithm
  • sexual selection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Engineering(all)
  • Statistics and Probability

Cite this

A hybrid of adaptive neuro-fuzzy inference system and genetic algorithm. / Varnamkhasti, M. Jalali; Hassan, Nasruddin.

In: Journal of Intelligent and Fuzzy Systems, Vol. 25, No. 3, 2013, p. 793-796.

Research output: Contribution to journalArticle

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