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dc.contributor.authorDockhorn, A
dc.contributor.authorLucas, S
dc.date.accessioned2024-07-11T11:45:54Z
dc.date.available2024-07-11T11:45:54Z
dc.date.issued2022-11-01
dc.identifier.citationA. Dockhorn and S. Lucas, "Choosing Representation, Mutation, and Crossover in Genetic Algorithms," in IEEE Computational Intelligence Magazine, vol. 17, no. 4, pp. 52-53, 1 Nov. 2022, doi: 10.1109/MCI.2022.3199626. keywords: {Evolutionary computation;Genetics;Genetic algorithms},en_US
dc.identifier.issn1556-603X
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/98016
dc.description.abstractThis paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.en_US
dc.format.extent52 - 53
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Computational Intelligence Magazine
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleChoosing Representation, Mutation, and Crossover in Genetic Algorithmsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/MCI.2022.3199626
pubs.issue4en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume17en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.projectb215eee3-195d-4c4f-a85d-169a4331c138en_US


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