Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/15124
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dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorWalker, Jamesen_UK
dc.contributor.authorHyde, Matthewen_UK
dc.contributor.authorCurtois, Timen_UK
dc.contributor.editorCoello, Coello CAen_UK
dc.contributor.editorCutello, Ven_UK
dc.contributor.editorDeb K, Ken_UK
dc.contributor.editorForrest, Sen_UK
dc.contributor.editorNicosia, Gen_UK
dc.contributor.editorPavone, Men_UK
dc.date.accessioned2016-12-03T02:55:03Z-
dc.date.available2016-12-03T02:55:03Z-
dc.date.issued2012en_UK
dc.identifier.urihttp://hdl.handle.net/1893/15124-
dc.description.abstractHyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationOchoa G, Walker J, Hyde M & Curtois T (2012) Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework. In: Coello CC, Cutello V, Deb K K, Forrest S, Nicosia G & Pavone M (eds.) Parallel Problem Solving from Nature - PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 7492. 12th International Conference, Taormina, Italy, 01.09.2012-05.09.2012. Amsterdam: Springer, pp. 418-427. http://link.springer.com/chapter/10.1007%2F978-3-642-32964-7_42#en_UK
dc.relation.ispartofseriesLecture Notes in Computer Science, Vol. 7492en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.rights.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_UK
dc.titleAdaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic frameworken_UK
dc.typeConference Paperen_UK
dc.rights.embargodate3000-08-31en_UK
dc.rights.embargoreason[Adaptive Evolutionary Algorithms.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.citation.issn0302-9743en_UK
dc.citation.spage418en_UK
dc.citation.epage427en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.identifier.urlhttp://link.springer.com/chapter/10.1007%2F978-3-642-32964-7_42#en_UK
dc.author.emailgabriela.ochoa@stir.ac.uken_UK
dc.citation.btitleParallel Problem Solving from Nature - PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part IIen_UK
dc.citation.conferencedates2012-09-01 - 2012-09-05en_UK
dc.citation.conferencelocationTaormina, Italyen_UK
dc.citation.conferencename12th International Conferenceen_UK
dc.citation.date30/09/2012en_UK
dc.citation.isbn978-3-642-32963-0en_UK
dc.publisher.addressAmsterdamen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.identifier.scopusid2-s2.0-84866390197en_UK
dc.identifier.wtid700072en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dcterms.dateAccepted2012-09-30en_UK
dc.date.filedepositdate2013-06-06en_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorWalker, James|en_UK
local.rioxx.authorHyde, Matthew|en_UK
local.rioxx.authorCurtois, Tim|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.contributorCoello, Coello CA|en_UK
local.rioxx.contributorCutello, V|en_UK
local.rioxx.contributorDeb K, K|en_UK
local.rioxx.contributorForrest, S|en_UK
local.rioxx.contributorNicosia, G|en_UK
local.rioxx.contributorPavone, M|en_UK
local.rioxx.freetoreaddate3000-08-31en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||en_UK
local.rioxx.filenameAdaptive Evolutionary Algorithms.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source978-3-642-32963-0en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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