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 L-
dc.contributor.authorOchoa, Gabriela-
dc.contributor.authorHyde, Matthew-
dc.contributor.authorFreitas, Alex A-
dc.contributor.authorWoodward, John-
dc.contributor.authorSwan, Jerry-
dc.date.accessioned2015-11-20T23:15:46Z-
dc.date.issued2014-03-
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.isoen-
dc.publisherSpringer-
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.-
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.-
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.embargodate2999-12-31T00:00:00Z-
dc.rights.embargoreasonThe 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.-
dc.identifier.doihttp://dx.doi.org/10.1007/s10710-013-9186-9-
dc.citation.jtitleGenetic Programming and Evolvable Machines-
dc.citation.issn1389-2576-
dc.citation.volume15-
dc.citation.issue1-
dc.citation.spage3-
dc.citation.epage35-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPublisher version (final published refereed version)-
dc.author.emailgabriela.ochoa@stir.ac.uk-
dc.citation.date18/04/2013-
dc.contributor.affiliationFederal University of Minas Gerais-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.contributor.affiliationUniversity of East Anglia-
dc.contributor.affiliationUniversity of Kent-
dc.contributor.affiliationComputing Science and Mathematics-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.rights.embargoterms2999-12-31-
dc.rights.embargoliftdate2999-12-31-
dc.identifier.isi000330968500002-
Appears in Collections:Computing Science and Mathematics Journal Articles

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