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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) shifts the burden of algorithm choice and design from developer to machine. Constructing an appropriate solver from a set of problem instances becomes a machine learning problem, with instances as training data. An efficient solver is trained for unseen problem instances with similar characteristics to those in the training set. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply a typical genetic programming ADA approach for bin packing problems to several new and existing public benchmark sets. Algorithms trained on some sets are general and apply well to most others, whereas some training sets result in highly specialised algorithms that do not generalise. We relate these findings to features (simple metrics) of instances. Using instance sets with narrowly-distributed features for training results in highly specialised algorithms, whereas those with well-spread features result in very general algorithms. We show that variance in certain features has a strong correlation with the generality of the trained policies. Our results provide further grounding for recent work using features to predict algorithm performance, and show the suitability of particular instance sets for training in ADA for bin packing. The data sets, including all computed features, the evolved policies, and their performances, and the visualisations for all feature sets, are available from
dc.publisherUniversity of Stirlingen_UK
dc.relationBrownlee A, Woodward JR & Veerapen N (2018) Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features (Detailed Experiments and Results). Not applicable. Stirling: University of Stirling.en_UK
dc.rightsAuthors retain copyright.en_UK
dc.subjectAutomatic design of algorithmsen_UK
dc.subjectbin packingen_UK
dc.titleRelating Training Instances to Automatic Design of Algorithms for Bin Packing via Features (Detailed Experiments and Results)en_UK
dc.typeTechnical Reporten_UK
dc.contributor.sponsorNot applicableen_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.description.notesWork funded by UK EPSRC [grants EP/N002849/1, EP/J017515/1]. Results obtained using the EPSRC funded ARCHIE-WeSt HPC [EPSRC grant EP/K000586/1].en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.relation.funderprojectFAIME: A Feature based Framework to Automatically Integrate and Improve Metaheuristics via Examples.en_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeTechnical Reporten_UK
local.rioxx.authorBrownlee, Alexander|0000-0003-2892-5059en_UK
local.rioxx.authorWoodward, John R|en_UK
local.rioxx.authorVeerapen, Nadarajen|0000-0003-3699-1080en_UK
local.rioxx.projectEP/N002849/1|Engineering and Physical Sciences Research Council|
local.rioxx.projectEP/J017515/1|Engineering and Physical Sciences Research Council|
Appears in Collections:Computing Science and Mathematics Technical Reports

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