A New Approach Method of Crossover Process Based On Genetic Algorithm Using High Dimensional Benchmark Functions

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dc.contributor.authorTunay, Mustafa
dc.date.accessioned2022-04-12T14:24:21Z
dc.date.available2022-04-12T14:24:21Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractThe design of the improved genetic algorithm (GA+) is based on a meta-heuristic search for optimization problems. In this paper, the crossover process in the original genetic algorithm is improved. The improvement of the crossover process is renewed by applying two conditions. One of them is keeping the last genes (constant) for each population; the second one is about rotating genes according to the defined range of points between each two selected populations. The improved genetic algorithm (GA+) has the possibility of accelerating local convergence. Therefore, it gets a chance to search for better values globally using these conditions. All processes in the improved genetic algorithm have been represented in this paper. The performance of the proposed algorithm is evaluated using 7 benchmark functions (test functions) on different dimensions. Ackley function, Rastrigin function and Holzman function are multi-modal minimization functions; Schwefel 2.22 function, Sphere function, Sum Squares function and Rosenbrock function are uni-modal minimization functions. These functions are evaluated by considering cases that are minimized by having a set of dimensions as 30, 60, and 90. Additionally, the performance of the GA+ is compared with the performance of comparative optimization algorithms (meta-heuristics). The comparative results have shown the performance of the GA+ that performs much better than others for optimization functions.en_US
dc.identifier.doi10.15864/ajse.2204en_US
dc.identifier.endpage33en_US
dc.identifier.issn2687-9530
dc.identifier.issn2687-9581
dc.identifier.issue2en_US
dc.identifier.startpage26en_US
dc.identifier.urihttps://doi.org/10.15864/ajse.2204
dc.identifier.urihttps://hdl.handle.net/11363/3475
dc.identifier.urihttps://doi.org/
dc.identifier.volume2en_US
dc.institutionauthorTunay, Mustafa
dc.language.isoenen_US
dc.relation.ispartofAmerican Journal of Science & Engineering (AJSE)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectGenetic algorithmsen_US
dc.subjectImproved crossover processen_US
dc.subjectMetaheuristic searchen_US
dc.subjectBenchmark functionsen_US
dc.titleA New Approach Method of Crossover Process Based On Genetic Algorithm Using High Dimensional Benchmark Functionsen_US
dc.typeArticleen_US

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