Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118732
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dc.contributor.authorKerschke, P.en
dc.contributor.authorHoos, H.en
dc.contributor.authorNeumann, F.en
dc.contributor.authorTrautmann, H.en
dc.date.issued2019en
dc.identifier.citationEvolutionary Computation, 2019; 27(1):3-45en
dc.identifier.issn1063-6560en
dc.identifier.issn1530-9304en
dc.identifier.urihttp://hdl.handle.net/2440/118732-
dc.description.abstractIt has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.en
dc.description.statementofresponsibilityPascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmannen
dc.language.isoenen
dc.publisherMassachusetts Institute of Technology Pressen
dc.rights© 2018 Massachusetts Institute of Technologyen
dc.subjectAutomated algorithm selection; automated algorithm configuration; combinatorial optimisation; continuous optimisation; data streams.; exploratory landscape analysis; feature-based approaches; machine learning; metalearningen
dc.titleAutomated algorithm selection: survey and perspectivesen
dc.typeJournal articleen
dc.identifier.rmid0030105206en
dc.identifier.doi10.1162/evco_a_00242en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP160102401en
dc.identifier.pubid451622-
pubs.library.collectionMathematical Sciences publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]en
Appears in Collections:Mathematical Sciences publications

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