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dc.contributor.authorOhira, Ryoma
dc.contributor.authorIslam, Md Saiful
dc.date.accessioned2020-11-15T22:18:23Z
dc.date.available2020-11-15T22:18:23Z
dc.date.issued2020
dc.identifier.isbn9781728169293
dc.identifier.doi10.1109/cec48606.2020.9185762
dc.identifier.urihttp://hdl.handle.net/10072/399280
dc.description.abstractThe island model allows genetic algorithms to effectively maintain diversity through migration between multiple independent populations. Due to its flexibility and modularity, it is commonly employed in distributed and parallel implementations, particularly in recent trends in leveraging the massively parallel cores in GPUs. However, the efficiency and effectiveness of the island model can be considered as its ability to manage its global and local search while minimising the overlap of islands searching in the same area of the solution space. This paper introduces a GPU accelerated island-model genetic algorithm that conducts global search by organising its populations into islands according to the similarity in genotype sequences. Local search is managed through adaptive mechanisms designed to maintain population diversity. The characteristics of the proposed genetic algorithm are investigated with encouraging results demonstrating its robustness and scalability when solving ordered optimisation problems.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2020 IEEE Congress on Evolutionary Computation (CEC 2020)
dc.relation.ispartofconferencetitle2020 IEEE Congress on Evolutionary Computation (CEC 2020)
dc.relation.ispartofdatefrom2020-07-19
dc.relation.ispartofdateto2020-07-24
dc.relation.ispartoflocationGlasgow, United Kingdom
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchOptimisation
dc.subject.fieldofresearchComputational complexity and computability
dc.subject.fieldofresearchcode460299
dc.subject.fieldofresearchcode490304
dc.subject.fieldofresearchcode461302
dc.titleGPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationOhira, R; Islam, MS, GPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems, 2020 IEEE Congress on Evolutionary Computation (CEC 2020), 2020
dc.date.updated2020-11-13T05:55:02Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 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.
gro.hasfulltextFull Text
gro.griffith.authorIslam, Saiful


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    Contains papers delivered by Griffith authors at national and international conferences.

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