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dc.contributor.authorChugh, Tinkle
dc.contributor.authorSindhya, Karthik
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorHakanen, Jussi
dc.contributor.authorJin, Yaochu
dc.date.accessioned2016-10-03T05:53:50Z
dc.date.available2016-10-03T05:53:50Z
dc.date.issued2016
dc.identifier.citationChugh, T., Sindhya, K., Miettinen, K., Hakanen, J., & Jin, Y. (2016). On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization. In J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), <em>Parallel Problem Solving from Nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings</em> (pp. 214-224). Lecture Notes in Computer Science, 9921. Springer International Publishing. <a href="http://dx.doi.org/10.1007/978-3-319-45823-6_20">doi:10.1007/978-3-319-45823-6_20</a>
dc.identifier.otherTUTKAID_71319
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/51494
dc.description.abstractSurrogate-assisted evolutionary multiobjective optimization algorithms are often used to solve computationally expensive problems. But their efficacy on handling constrained optimization problems having more than three objectives has not been widely studied. Particularly the issue of how feasible and infeasible solutions are handled in generating a data set for training a surrogate has not received much attention. In this paper, we use a recently proposed Kriging-assisted evolutionary algorithm for many-objective optimization and investigate the effect of infeasible solutions on the performance of the surrogates. We assume that constraint functions are computationally inexpensive and consider different ways of handling feasible and infeasible solutions for training the surrogate and examine them on different benchmark problems. Results on the comparison with a reference vector guided evolutionary algorithm show that it is vital for the success of the surrogate to properly deal with infeasible solutions.
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofParallel Problem Solving from Nature PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings, ISBN 978-3-319-45822-9
dc.relation.ispartofseriesLecture Notes in Computer Science;9921
dc.subject.othermultiobjective optimization
dc.subject.otherdecision making
dc.subject.othercomputational cost
dc.subject.othermetamodel
dc.subject.otherevolution control
dc.titleOn Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201609304240
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2016-09-30T12:15:05Z
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange214-224
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.doi10.1007/978-3-319-45823-6_20


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