Precision constrained optimization by exponential ranking

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dc.contributor.authorBittermann, Michael S.
dc.contributor.authorÇiftçioğlu, Özer
dc.date.accessioned2024-07-12T20:57:08Z
dc.date.available2024-07-12T20:57:08Z
dc.date.issued2016en_US
dc.departmentFakülteler, Mimarlık ve Tasarım Fakültesi, Mimarlık Bölümüen_US
dc.descriptionIEEE Computational Intelligence Society (CIS)en_US
dc.description2016 IEEE Congress on Evolutionary Computation, CEC 2016 -- 24 July 2016 through 29 July 2016 -- -- 124911en_US
dc.description.abstractDemonstrative results of a probabilistic constraint handling approach that is exclusively using evolutionary computation are presented. In contrast to other works involving the same probabilistic considerations, in this study local search has been omitted, in order to assess the necessity of this deterministic local search procedure in connection with the evolutionary one. The precision stems from the non-linear probabilistic distance measure that maintains stable evolutionary selection pressure towards the feasible region throughout the search, up to micro level in the range of 10 -10 or beyond. The details of the theory are revealed in another paper [1]. In this paper the implementation results are presented, where the non-linear distance measure is used in the ranking of the solutions for effective tournament selection. The test problems used are selected from the existing literature. The evolutionary implementation without local search turns out to be already competitively accurate with sophisticated and accurate state-of-the-art constrained optimization algorithms. This indicates the potential for enhancement of the sophisticated algorithms, as to their precision and accuracy, by the integration of the proposed approach. © 2016 IEEE.en_US
dc.identifier.citationBittermann, M. S. ve Çiftçioğlu, Ö. (2016). Precision constrained optimization by exponential ranking. 2016 IEEE Congress on Evolutionary Computation, CEC 2016. s. 2296-2305.en_US
dc.identifier.doi10.1109/CEC.2016.7744072
dc.identifier.endpage2305en_US
dc.identifier.isbn9781509006229
dc.identifier.scopus2-s2.0-85008248865en_US
dc.identifier.startpage2296en_US
dc.identifier.urihttps://dx.doi.org/10.1109/CEC.2016.7744072
dc.identifier.urihttps://hdl.handle.net/20.500.12415/3074
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2016 IEEE Congress on Evolutionary Computation, CEC 2016en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY06935
dc.subjectConstrained optimizationen_US
dc.subjectEvolutionary algorithmen_US
dc.subjectMultiobjective optimizationen_US
dc.subjectProbabilistic modelingen_US
dc.titlePrecision constrained optimization by exponential rankingen_US
dc.typeConference Object
dspace.entity.typePublication

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