A fuzzy genetic algorithm based on binary encoding for solving multidimensional knapsack problems
type: Item
instance of: scholarly article
title: A fuzzy genetic algorithm based on binary encoding for solving multidimensional knapsack problems
zbMATH Open document ID: 1244.90257
DOI: 10.1155/2012/703601
published in: Journal of Applied Mathematics
publication date: 2012-08-06T00:00:00Z
review text: Summary: The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focuses on controlling the genetic parameters by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with other genetic operators, heuristics, and local search algorithms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.
Mathematics Subject Classification ID: 90C70
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 93C42
Mathematics Subject Classification ID: 90C27
Mathematics Subject Classification ID: 90C09
zbMATH DE Number: 6063459
zbMATH Keywords: fuzzy genetic algorithm
zbMATH Keywords: multidimensional knapsack problem
zbMATH Keywords: fuzzy logic control
describes a project that uses: Knapsack
describes a project that uses: OR-Library
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/703601
OpenAlex ID: W1971842476
Wikidata QID: Q58907191
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DBLP publication ID: journals/jam/VarnamkhastiL12
author: M. Jalali Varnamkhasti
author: L. S. Lee
description: scientific article; zbMATH DE number 6063459