Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24077
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dc.contributor.authorHong, Libinen_UK
dc.contributor.authorDrake, Johnen_UK
dc.contributor.authorWoodward, Johnen_UK
dc.contributor.authorOzcan, Enderen_UK
dc.date.accessioned2017-11-10T04:22:54Z-
dc.date.available2017-11-10T04:22:54Z-
dc.date.issued2016en_UK
dc.identifier.urihttp://hdl.handle.net/1893/24077-
dc.description.abstractIn this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes.en_UK
dc.language.isoenen_UK
dc.publisherACMen_UK
dc.relationHong L, Drake J, Woodward J & Ozcan E (2016) Automatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-Heuristic In: GECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016. GECCO '16: Genetic and Evolutionary Computation Conference 2016, New York, 20.07.2016-24.07.2016. New York: ACM, pp. 725-732. https://doi.org/10.1145/2908812.2908958; https://doi.org/10.1145/2908812.2908958.en_UK
dc.relation.urihttp://gecco-2016.sigevo.org/index.html/HomePage#&panel1-5en_UK
dc.rightsPublisher policy allows this work to be made available in this repository. Published in GECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016, Pages 725-732 by ACM. The original publication is available at: http://dx.doi.org/10.1145/2908812.2908958en_UK
dc.titleAutomatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-Heuristicen_UK
dc.typeConference Paperen_UK
dc.identifier.doi10.1145/2908812.2908958en_UK
dc.citation.spage725en_UK
dc.citation.epage732en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.identifier.urlhttps://doi.org/10.1145/2908812.2908958en_UK
dc.author.emailjrw@cs.stir.ac.uken_UK
dc.citation.btitleGECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016en_UK
dc.citation.conferencedates2016-07-20 - 2016-07-24en_UK
dc.citation.conferencelocationDenver, CO, USAen_UK
dc.citation.conferencenameGECCO '16: Genetic and Evolutionary Computation Conference 2016en_UK
dc.citation.isbn978-1-4503-4206-3en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationUniversity of Nottingham Ningbo Chinaen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.identifier.isi000382659200092en_UK
dc.identifier.scopusid2-s2.0-84985920063en_UK
dc.identifier.wtid552089en_UK
dc.contributor.orcid0000-0002-2093-8990en_UK
dc.date.accepted2016-06-22en_UK
dc.date.firstcompliantdepositdate2016-08-22en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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