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dc.contributor.authorBaritompa, W.en_US
dc.contributor.authorDur, M.en_US
dc.contributor.authorHendrix, E.en_US
dc.contributor.authorNoakes, L.en_US
dc.contributor.authorPullan, Wayneen_US
dc.contributor.authorWood, G.en_US
dc.contributor.editorP.M. Pardalosen_US
dc.date.accessioned2017-04-24T11:12:16Z
dc.date.available2017-04-24T11:12:16Z
dc.date.issued2005en_US
dc.date.modified2010-08-20T06:26:02Z
dc.identifier.issn09255001en_US
dc.identifier.doi10.1007/s10898-004-9968-yen_AU
dc.identifier.urihttp://hdl.handle.net/10072/4283
dc.description.abstractLarge scale optimisation problems are frequently solved using stochastic methods. Such methods often generate points randomly in a search region in a neighbourhood of the current point, backtrack to get past barriers and employ a local optimiser. The aim of this paper is to explore how these algorithmic components should be used, given a particular objective function landscape. In a nutshell, we begin to provide rules for efficient travel, if we have some knowledge of the large or small scale geometry.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom579en_US
dc.relation.ispartofpageto598en_US
dc.relation.ispartofissue4en_US
dc.relation.ispartofjournalJournal of Global Optimizationen_US
dc.relation.ispartofvolume31en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode230118en_US
dc.titleMatching Stochastic Algorithms to Objective Function Landscapesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2005
gro.hasfulltextNo Full Text


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