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dc.contributor.authorMajumdar, A
dc.contributor.authorChugh, T
dc.contributor.authorMiettinen, K
dc.contributor.authorLopez-Ibanez, M
dc.date.accessioned2019-03-04T14:04:33Z
dc.date.issued2019-02-03
dc.description.abstractMany works on surrogate-assisted evolutionary multiobjective optimization have been devoted to problems where function evaluations are time-consuming (e.g., based on simulations). In many real-life optimization problems, mathematical or simulation models are not always available and, instead, we only have data from experiments, measurements or sensors. In such cases, optimization is to be performed on surrogate models built on the data available. The main challenge there is to fit an accurate surrogate model and to obtain meaningful solutions. We apply Kriging as a surrogate model and utilize corresponding uncertainty information in different ways during the optimization process. We discuss experimental results obtained on benchmark multiobjective optimization problems with different sampling techniques and numbers of objectives. The results show the effect of different ways of utilizing uncertainty information on the quality of solutions.en_GB
dc.description.sponsorshipNatural Environment Research Council (NERC)en_GB
dc.identifier.citationEvolutionary Multi-Criterion Optimization (EMO 2019), 10 - 13 March 2019, East Lansing, MI, USA, pp. 463 - 474en_GB
dc.identifier.doi10.1007/978-3-030-12598-1_37
dc.identifier.grantnumberNE/P017436/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/36242
dc.language.isoenen_GB
dc.publisherSpringer Verlagen_GB
dc.rights© Springer Nature Switzerland AG 2019en_GB
dc.subjectmachine learningen_GB
dc.subjectevolutionary computationen_GB
dc.subjectmetaheuristicsen_GB
dc.subjectoptimizationen_GB
dc.subjectmulti-objective optimizationen_GB
dc.subjectPareto optimalityen_GB
dc.subjectsupervised learningen_GB
dc.titleOn dealing with uncertainties from Kriging models in offline data-driven evolutionary multiobjective optimizationen_GB
dc.typeConference proceedingsen_GB
dc.date.available2019-03-04T14:04:33Z
dc.contributor.editorDeb, Ken_GB
dc.identifier.issn0302-9743
dc.descriptionThis is the author accepted manuscript. The final version is available from Springer via the DOI in this record.en_GB
dc.identifier.journalLecture Notes in Computer Scienceen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2019-02-03
exeter.funder::Natural Environment Research Council (NERC)en_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2019-02-03
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2019-03-04T13:28:38Z
refterms.versionFCDAM
refterms.dateFOA2019-03-04T14:04:36Z
refterms.panelBen_GB


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