Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23394
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dc.contributor.authorWoodward, Johnen_UK
dc.contributor.authorJohnson, Colinen_UK
dc.contributor.authorBrownlee, Alexanderen_UK
dc.contributor.editorFriedrich, Ten_UK
dc.date.accessioned2017-11-10T01:40:47Z-
dc.date.available2017-11-10T01:40:47Z-
dc.date.issued2016en_UK
dc.identifier.urihttp://hdl.handle.net/1893/23394-
dc.description.abstractAutomatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improvement them.  These methods form a continuum of methodologies, from the “limited” ability to change (for example only the parameters) to the “complete” ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH) to automatically design algorithms, and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approachesen_UK
dc.language.isoenen_UK
dc.publisherACMen_UK
dc.relationWoodward J, Johnson C & Brownlee A (2016) Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming. In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. GECCO 2016: Genetic and Evolutionary Computation Conference, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1357-1358. https://doi.org/10.1145/2908961.2931728en_UK
dc.rightsPublisher policy allows this work to be made available in this repository. Published in GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion by ACM. The original publication is available at: http://dl.acm.org/citation.cfm?id=2931728&CFID=823928677&CFTOKEN=80769513en_UK
dc.subjectGenetic Improvement (GI)en_UK
dc.subjectGenetic Programming (GP)en_UK
dc.titleConnecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programmingen_UK
dc.typeConference Paperen_UK
dc.identifier.doi10.1145/2908961.2931728en_UK
dc.citation.spage1357en_UK
dc.citation.epage1358en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedUnrefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailab90@cs.stir.ac.uken_UK
dc.citation.btitleGECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companionen_UK
dc.citation.conferencedates2016-07-20 - 2016-07-24en_UK
dc.citation.conferencelocationDenver, CO, USAen_UK
dc.citation.conferencenameGECCO 2016: Genetic and Evolutionary Computation Conferenceen_UK
dc.citation.date31/07/2016en_UK
dc.citation.isbn978-1-4503-4323-7en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationUniversity of Kenten_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000383741800187en_UK
dc.identifier.scopusid2-s2.0-84986246013en_UK
dc.identifier.wtid564590en_UK
dc.contributor.orcid0000-0002-2093-8990en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.date.accepted2016-04-18en_UK
dc.date.filedepositdate2016-06-21en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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