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dc.contributor.authorMaree, Armand
dc.contributor.authorRiekert, Marius
dc.contributor.authorHelbig, Mardé
dc.contributor.editorRutkowski, Leszek
dc.contributor.editorScherer, Rafa L
dc.contributor.editorKorytkowski, Marcin
dc.contributor.editorPedrycz, Witold
dc.contributor.editorTadeusiewicz, Ryszard
dc.contributor.editorZurada, Jacek M
dc.date.accessioned2021-01-29T03:00:57Z
dc.date.available2021-01-29T03:00:57Z
dc.date.issued2018
dc.identifier.isbn978-3-319-91252-3
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-91253-0_43
dc.identifier.urihttp://hdl.handle.net/10072/401556
dc.description.abstractGenetic Programming is a specialized form of genetic algorithms which evolve trees. This paper proposes an approach to evolve an expression tree, which is an N-Ary tree that represents a mathematical equation and that describes a given set of points in some space. The points are a set of trade-off solutions of a multi-objective optimization problem (MOOP), referred to as the Pareto Optimal Front (POF). The POF is a curve in a multi-dimensional space that describes the boundary where a single objective in a set of objectives cannot improve more without sacrificing the optimal value of the other objectives. The algorithm, proposed in this paper, will thus find the mathematical function that describes a POF after a multi-objective optimization algorithm (MOA) has solved a MOOP. Obtaining the equation will assist in finding other points on the POF that was not discovered by the MOA. Results indicate that the proposed algorithm matches the general curve of the points, although the algorithm sometimes struggles to match the points perfectly.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSpringer
dc.relation.ispartofconferencename17th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2018)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science
dc.relation.ispartofdatefrom2018-06-03
dc.relation.ispartofdateto2018-06-07
dc.relation.ispartoflocationZakopane, Poland
dc.relation.ispartofpagefrom462
dc.relation.ispartofpageto473
dc.relation.ispartofvolume10841
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsMulti-objective optimization
dc.titleDeriving Functions for Pareto Optimal Fronts Using Genetic Programming
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationMaree, A; Riekert, M; Helbig, M, Deriving Functions for Pareto Optimal Fronts Using Genetic Programming, Lecture Notes in Computer Science, 2018, 10841, pp. 462-473
dc.date.updated2021-01-29T02:59:25Z
gro.hasfulltextNo Full Text
gro.griffith.authorHelbig, Mardé


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