A genetic algorithm with fuzzy crossover operator and probability
type: Item
instance of: scholarly article
title: A genetic algorithm with fuzzy crossover operator and probability
zbMATH Open document ID: 1236.90150
DOI: 10.1155/2012/956498
author: M. Jalali Varnamkhasti
author: L. S. Lee
author: M. R. Abu Bakar
author: W. J. Leong
published in: Advances in Operations Research
publication date: 2012-03-08T00:00:00Z
review text: Summary: The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probability selection technique is proposed based on the population diversity using a fuzzy logic controller. The measurement of the population diversity is based on the genotype and phenotype properties. In this fuzzy inference system, the selection of the crossover operator and its probability are controlled by a set of fuzzy rules derived from the fuzzy logic controller. Extensive computational experiments are conducted on the proposed algorithm, and the results are compared with some crossover operators commonly used for solving multidimensional 0/1 knapsack problems published in the literature. The results indicate that the proposed algorithm is effective in finding better quality solutions.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C27
Mathematics Subject Classification ID: 90C70
zbMATH DE Number: 6013024
zbMATH Keywords: crossover operator
zbMATH Keywords: genotype and phenotype properties
zbMATH Keywords: fuzzy rules
Wikidata QID: Q58698870
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/956498
OpenAlex ID: W1987822801
cites work: Q3515603
cites work: Artificial Evolution
cites work: Q4736889
cites work: Q4449726
cites work: A formal analysis of the role of multi-point crossover in genetic algorithms
cites work: Q3493254
cites work: Optimization of control parameters in genetic algorithms: A stochastic approach
cites work: Evolution of appropriate crossover and mutation operators in a genetic process
cites work: Machine Learning: ECML 2004
cites work: A genetic algorithm for the multidimensional knapsack problem
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description: scientific article; zbMATH DE number 6013024