Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27082
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBrownlee, Alexanderen_UK
dc.contributor.authorWoodward, John Ren_UK
dc.contributor.authorVeerapen, Nadarajenen_UK
dc.date.accessioned2018-04-20T04:01:59Z-
dc.date.available2018-04-20T04:01:59Zen_UK
dc.date.issued2018-12-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/27082-
dc.description.abstractAutomatic Design of Algorithms (ADA) treats algorithm choice and design as a machine learning problem, with problem instances as training data. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply genetic programming ADA for bin packing to sev- eral new and existing benchmark sets. Using sets with narrowly- distributed features for training results in highly specialised al- gorithms, whereas those with well-spread features result in very general algorithms. Variance in certain features has a strong corre- lation with the generality of the trained policies.en_UK
dc.language.isoenen_UK
dc.publisherACMen_UK
dc.relationBrownlee A, Woodward JR & Veerapen N (2018) Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features. In: Proceedings of GECCO 2018. Genetic and Evolutionary Computation Conference 2018, 15.07.2018-19.07.2018. New York: ACM, pp. 135-136. https://doi.org/10.1145/3205651.3205748en_UK
dc.relationFAIME: A Feature based Framework to Automatically Integrate and Improve Metaheuristics via Examples.en_UK
dc.relationEP/N002849/1en_UK
dc.relationDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
dc.relationEP/J017515/1en_UK
dc.relation.urihttp://hdl.handle.net/11667/108en_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. Publisher policy allows this work to be made available in this repository. Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion by ACM. The original publication is available at: https://doi.org/10.1145/3205651.3205748en_UK
dc.subjectAutomatic design of algorithmsen_UK
dc.subjectfeaturesen_UK
dc.subjectbin packingen_UK
dc.titleRelating Training Instances to Automatic Design of Algorithms for Bin Packing via Featuresen_UK
dc.typeConference Paperen_UK
dc.rights.embargoreason[relating-training-instances-forrepository.pdf] : Until this work is formally published there will be an embargo on the full text of this work.en_UK
dc.identifier.doi10.1145/3205651.3205748en_UK
dc.citation.spage135en_UK
dc.citation.epage136en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailalexander.brownlee@stir.ac.uken_UK
dc.citation.btitleProceedings of GECCO 2018en_UK
dc.citation.conferencedates2018-07-15 - 2018-07-19en_UK
dc.citation.conferencelocationNew Yorken_UK
dc.citation.conferencenameGenetic and Evolutionary Computation Conference 2018en_UK
dc.citation.date31/07/2018en_UK
dc.citation.isbn978-1-4503-5764-7en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.wtid877496en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.contributor.orcid0000-0003-3699-1080en_UK
dc.date.accepted2018-03-24en_UK
dc.date.firstcompliantdepositdate2018-04-18en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

Files in This Item:
File Description SizeFormat 
relating-training-instances-forrepository.pdfFulltext - Accepted Version339.51 kBAdobe PDFView/Open


This item is protected by original copyright



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

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.