Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/1695
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dc.contributor.authorHakimi, Danladi-
dc.contributor.authorOyewola, David O.-
dc.contributor.authorYahaya, Yusuph-
dc.contributor.authorBolarin, Gbolahan-
dc.date.accessioned2021-06-06T10:30:40Z-
dc.date.available2021-06-06T10:30:40Z-
dc.date.issued2016-09-
dc.identifier.citationHakimi, D., Oyewola, D.O.,Yahaya, Y. and Bolarin, G. (2016), Comparative Analysis of Genetic Crossover Operators in Knapsack Problem, J. Appl. Sci. Environ. Management, Vol. 20 (3) 593-596, http://dx.doi.org/10.4314/jasem.v20i3.13en_US
dc.identifier.issn1119-8362-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/1695-
dc.description.abstractThe Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.en_US
dc.language.isoenen_US
dc.publisherJournal of Applied Sciences and Environmental Managementen_US
dc.subjectGenetic Algorithmsen_US
dc.titleComparative Analysis of Genetic Crossover Operators in Knapsack Problemen_US
dc.typeArticleen_US
Appears in Collections:Mathematics

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