Show simple item record

Multi-objective optimisation in the presence of uncertainty

dc.contributor.authorFieldsend, Jonathan E.
dc.contributor.authorEverson, Richard M.
dc.date.accessioned2013-07-09T15:38:43Z
dc.date.issued2005-12-12
dc.description.abstractThere has been only limited discussion on the effect of uncertainty and noise in multi-objective optimisation problems and how to deal with it. Here we address this problem by assessing the probability of dominance and maintaining an archive of solutions which are, with some known probability, mutually non-dominating.We examine methods for estimating the probability of dominance. These depend crucially on estimating the effective noise variance and we introduce a novel method of learning the variance during optimisation.Probabilistic domination contours are presented as a method for conveying the confidence that may be placed in objectives that are optimised in the presence of uncertainty.en_GB
dc.identifier.citation2005 IEEE Congress on Evolutionary Computation vol. 1, pp. 243-250en_GB
dc.identifier.doi10.1109/CEC.2005.1554691
dc.identifier.urihttp://hdl.handle.net/10871/11661
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.relation.urlhttps://github.com/fieldsend/ieee_cec_2005_bayes_uncertainen_GB
dc.rightsCopyright © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectestimation theoryen_GB
dc.subjectevolutionary computationen_GB
dc.subjectnoiseen_GB
dc.subjectoptimisationen_GB
dc.subjectprobabilityen_GB
dc.subjectComputer aided manufacturingen_GB
dc.subjectDesign optimizationen_GB
dc.subjectError analysisen_GB
dc.subjectMeasurement errorsen_GB
dc.subjectOptimization methodsen_GB
dc.subjectPattern recognitionen_GB
dc.subjectStochastic systemsen_GB
dc.subjectUncertaintyen_GB
dc.titleMulti-objective optimisation in the presence of uncertaintyen_GB
dc.typeConference paperen_GB
dc.date.available2013-07-09T15:38:43Z
dc.identifier.isbn0780393635
dc.description2005 IEEE Congress on Evolutionary Computation, Edinburgh, Scotland, 2-5 September 2005en_GB
dc.descriptionThe codebase for this paper is available at https://github.com/fieldsend/ieee_cec_2005_bayes_uncertain


Files in this item

Name:
multi_objective_optimisation_2 ...
Size:
477.2Kb
Format:
PDF
Description:
Post-print
View/Open

This item appears in the following Collection(s)

Show simple item record