Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24924
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSoria-Alcaraz, Jorge Aen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorSotelo-Figeroa, Marcoen_UK
dc.contributor.authorBurke, Edmund Ken_UK
dc.date.accessioned2018-01-20T06:56:20Z-
dc.date.available2018-01-20T06:56:20Z-
dc.date.issued2017-08-01en_UK
dc.identifier.urihttp://hdl.handle.net/1893/24924-
dc.description.abstractWe address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic usingmulti-armed banditscoupled with a change detection mechanism. The methodology is tested on two real-world optimisation problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationSoria-Alcaraz JA, Ochoa G, Sotelo-Figeroa M & Burke EK (2017) A methodology for determining an effective subset of heuristics in selection hyper-heuristics. European Journal of Operational Research, 260 (3), pp. 972-983. https://doi.org/10.1016/j.ejor.2017.01.042en_UK
dc.rightsThis item has been embargoed for a period. During the embargo 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. Accepted refereed manuscript of: Soria-Alcaraz JA, Ochoa G, Sotelo-Figeroa M & Burke EK (2017) A methodology for determining an effective subset of heuristics in selection hyper-heuristics, European Journal of Operational Research, 260 (3), pp. 972-983. DOI: 10.1016/j.ejor.2017.01.042 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectMetaheuristicsen_UK
dc.subjectHyper-heuristicsen_UK
dc.subjectAdaptive Searchen_UK
dc.subjectCombinatorial optimisationen_UK
dc.subjectIterated Local Searchen_UK
dc.titleA methodology for determining an effective subset of heuristics in selection hyper-heuristicsen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2019-01-31en_UK
dc.rights.embargoreason[EJORMethodologySelHH2017 (1).pdf] Publisher requires embargo of 24 months after formal publication.en_UK
dc.identifier.doi10.1016/j.ejor.2017.01.042en_UK
dc.citation.jtitleEuropean Journal of Operational Researchen_UK
dc.citation.issn0377-2217en_UK
dc.citation.volume260en_UK
dc.citation.issue3en_UK
dc.citation.spage972en_UK
dc.citation.epage983en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailgabriela.ochoa@cs.stir.ac.uken_UK
dc.citation.date30/01/2017en_UK
dc.contributor.affiliationTechnological Institute of Leonen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversidad de Guanajuatoen_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.identifier.isiWOS:000399628200015en_UK
dc.identifier.scopusid2-s2.0-85011993339en_UK
dc.identifier.wtid536527en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dc.date.accepted2017-01-25en_UK
dcterms.dateAccepted2017-01-25en_UK
dc.date.filedepositdate2017-02-01en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorSoria-Alcaraz, Jorge A|en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorSotelo-Figeroa, Marco|en_UK
local.rioxx.authorBurke, Edmund K|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2019-01-31en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2019-01-30en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by-nc-nd/4.0/|2019-01-31|en_UK
local.rioxx.filenameEJORMethodologySelHH2017 (1).pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0377-2217en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

Files in This Item:
File Description SizeFormat 
EJORMethodologySelHH2017 (1).pdfFulltext - Accepted Version329.52 kBAdobe PDFView/Open


This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.