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dc.contributor.authorBrownlee, Alexanderen_UK
dc.contributor.authorWoodward, John Ren_UK
dc.contributor.authorVeerapen, Nadarajenen_UK
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.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.
dc.relationFAIME: A Feature based Framework to Automatically Integrate and Improve Metaheuristics via Examples.en_UK
dc.relationDAASE: Dynamic Adaptive Automated Software Engineeringen_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:
dc.subjectAutomatic design of algorithmsen_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.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_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.publisher.addressNew Yorken_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.contributor.affiliationComputing Scienceen_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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