Evolving fuzzy rules: evaluation of a new approach

Simple item page
dc.contributor.authorGhandar, A.
dc.contributor.authorMichalewicz, Z.
dc.contributor.authorNeumann, F.
dc.contributor.conferenceInternational Conference on Simulated Evolution and Learning (2010 : India)
dc.contributor.editorDeb, K.
dc.contributor.editorBhattacharya, A.
dc.contributor.editorChakraborti, N.
dc.contributor.editorChakroborty, P.
dc.contributor.editorDas, S.
dc.contributor.editorDutta, J.
dc.contributor.editorGupta, S.K.
dc.contributor.editorJain, A.
dc.contributor.editorAggarwal, V.
dc.contributor.editorBranke, J.
dc.contributor.editorLouis, S.J.
dc.contributor.editorTan, K.C.
dc.date.issued2010
dc.description.abstractEvolutionary algorithms have been successfully applied to optimize the rulebase of fuzzy systems. This has lead to powerful automated systems for financial applications. We experimentally evaluate the approach of learning fuzzy rules by evolutionary algorithms proposed by Kroeske et al. [10]. The results presented in this paper show that the optimization of fuzzy rules may be universally simplified regardless of the complex fitness surface for the overall optimization process. We incorporate a local search procedure that makes use of these theoretical results into an evolutionary algorithms for rule-base optimization. Our experimental results show that this improves a state of the art approach for financial applications. © 2010 Springer-Verlag.
dc.description.statementofresponsibilityAdam Ghandar, Zbigniew Michalewicz and Frank Neumann
dc.description.urihttp://portal.acm.org/citation.cfm?id=1947487
dc.identifier.citationSEAL '10 Proceedings of 8th International Conference on Simulated Evolution and Learning (SEAL 2010), 2010: pp.250-259
dc.identifier.doi10.1007/978-3-642-17298-4_26
dc.identifier.isbn9783642172977
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/64238
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0985723
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0985723
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsCopyright 2010 Springer-Verlag Berlin, Heidelberg
dc.source.urihttps://doi.org/10.1007/978-3-642-17298-4_26
dc.titleEvolving fuzzy rules: evaluation of a new approach
dc.typeConference paper
pubs.publication-statusPublished

Files

Collections

Computer Science publications