Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/18245
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dc.contributor.authorPappa, Gisele Len_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorHyde, Matthewen_UK
dc.contributor.authorFreitas, Alex Aen_UK
dc.contributor.authorWoodward, Johnen_UK
dc.contributor.authorSwan, Jerryen_UK
dc.date.accessioned2015-11-20T23:15:46Z-
dc.date.available2015-11-20T23:15:46Z-
dc.date.issued2014-03en_UK
dc.identifier.urihttp://hdl.handle.net/1893/18245-
dc.description.abstractThe fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to promote awareness and cross-fertilisation of ideas across the (by and large, non-overlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationPappa GL, Ochoa G, Hyde M, Freitas AA, Woodward J & Swan J (2014) Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genetic Programming and Evolvable Machines, 15 (1), pp. 3-35. https://doi.org/10.1007/s10710-013-9186-9en_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.subjectHyper-heuristicsen_UK
dc.subjectMeta-learningen_UK
dc.subjectGenetic programmingen_UK
dc.subjectAutomated algorithm designen_UK
dc.titleContrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithmsen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-19en_UK
dc.rights.embargoreason[Pappa et al 2014.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.identifier.doi10.1007/s10710-013-9186-9en_UK
dc.citation.jtitleGenetic Programming and Evolvable Machinesen_UK
dc.citation.issn1573-7632en_UK
dc.citation.issn1389-2576en_UK
dc.citation.volume15en_UK
dc.citation.issue1en_UK
dc.citation.spage3en_UK
dc.citation.epage35en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailgabriela.ochoa@stir.ac.uken_UK
dc.citation.date18/04/2013en_UK
dc.contributor.affiliationFederal University of Minas Geraisen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of East Angliaen_UK
dc.contributor.affiliationUniversity of Kenten_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000330968500002en_UK
dc.identifier.scopusid2-s2.0-84876134712en_UK
dc.identifier.wtid700052en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dc.contributor.orcid0000-0002-2093-8990en_UK
dcterms.dateAccepted2013-04-18en_UK
dc.date.filedepositdate2014-01-10en_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorPappa, Gisele L|en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorHyde, Matthew|en_UK
local.rioxx.authorFreitas, Alex A|en_UK
local.rioxx.authorWoodward, John|0000-0002-2093-8990en_UK
local.rioxx.authorSwan, Jerry|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2999-12-19en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||en_UK
local.rioxx.filenamePappa et al 2014.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1389-2576en_UK
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