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dc.contributor.authorSaini, Bhupinder Singh
dc.contributor.authorLopez-Ibanez, Manuel
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2019-09-02T05:50:45Z
dc.date.available2019-09-02T05:50:45Z
dc.date.issued2019
dc.identifier.citationSaini, B. S., Lopez-Ibanez, M., & Miettinen, K. (2019). Automatic surrogate modelling technique selection based on features of optimization problems. In <i>GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume</i> (pp. 1765-1772). ACM. <a href="https://doi.org/10.1145/3319619.3326890" target="_blank">https://doi.org/10.1145/3319619.3326890</a>
dc.identifier.otherCONVID_32153347
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/65389
dc.description.abstractA typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem. In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. When given a new dataset, features are used to select the most appropriate surrogate modelling technique. The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection.en
dc.format.extent2075
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofGECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume
dc.rightsIn Copyright
dc.subject.othersurrogate modelling
dc.subject.otherautomatic algorithm selection
dc.subject.otherexploratory landscape analysis
dc.titleAutomatic surrogate modelling technique selection based on features of optimization problems
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201909023994
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.contributor.oppiaineComputational Scienceen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-1-4503-6748-6
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1765-1772
dc.type.versionacceptedVersion
dc.rights.copyright© 2019 Association for Computing Machinery
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceGenetic and Evolutionary Computation Conference
dc.relation.grantnumber287496
dc.subject.ysooptimointi
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoalgoritmit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1145/3319619.3326890
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis research was partly supported by the Academy of Finland (grant number 287496, project DESDEO). This related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyvaskyla.
dc.type.okmA4


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