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A multiple mitosis genetic algorithm

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dc.citedby5
dc.contributor.authorKamil K.en_US
dc.contributor.authorChong K.H.en_US
dc.contributor.authorHashim H.en_US
dc.contributor.authorShaaya S.A.en_US
dc.contributor.authorid57195622807en_US
dc.contributor.authorid36994481200en_US
dc.contributor.authorid56644250200en_US
dc.contributor.authorid16022846200en_US
dc.date.accessioned2023-05-29T07:29:16Z
dc.date.available2023-05-29T07:29:16Z
dc.date.issued2019
dc.description.abstractGenetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum. This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum. � 2019 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijai.v8.i3.pp252-258
dc.identifier.epage258
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85073502669
dc.identifier.spage252
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073502669&doi=10.11591%2fijai.v8.i3.pp252-258&partnerID=40&md5=d7bf43520090f9e6e9e56421d5044db7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24946
dc.identifier.volume8
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleIAES International Journal of Artificial Intelligence
dc.titleA multiple mitosis genetic algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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