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dc.contributor.authorRandall, Marcusen_US
dc.contributor.authorHendtlass, Timen_US
dc.contributor.authorLewis, Andrewen_US
dc.contributor.editorAndrew Lewis, Sanaz Mostaghim and Marcus Randallen_US
dc.date.accessioned2017-04-24T09:52:57Z
dc.date.available2017-04-24T09:52:57Z
dc.date.issued2009en_US
dc.date.modified2010-08-19T07:04:52Z
dc.identifier.isbn9783642012617en_US
dc.identifier.doi10.1007/978-3-642-01262-4_6en_AU
dc.identifier.urihttp://hdl.handle.net/10072/29401
dc.description.abstractExtremal optimisation is an emerging nature inspired meta-heuristic search technique that allows a poorly performing solution component to be removed at each iteration of the algorithm and replaced by a random value. This creates opportunity for assignment type problems as it enables a component to be moved to a more appropriate group. This may then drive the system towards an optimal solution. In this chapter, the general capabilities of extremal optimisation, in terms of assignment type problems, are explored. In particular, we provide an analysis of the moves selected by extremal optimisation and show that it does not suffer from premature convergence. Following this we develop a model of extremal optimisation that includes techniques to a) process constraints by allowing the search to move between feasible and infeasible space, b) provide a generic partial feasibility restoration heuristic to drive the solution towards feasible space, and c) develop a population model of the meta-heuristic that adaptively removes and introduces new members in accordance with the principles of self-organised criticality. A range of computational experiments on prototypical assignment problems, namely generalised assignment, bin packing, and capacitated hub location, indicate that extremal optimisation can form the foundation for a powerful and competitive meta-heuristic for this class of problems.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringer-Verlagen_US
dc.publisher.placeBerlinen_US
dc.publisher.urihttp://www.springerlink.com/en_AU
dc.relation.ispartofbooktitleBiologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applicationsen_US
dc.relation.ispartofchapter6en_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom139en_US
dc.relation.ispartofpageto164en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchOptimisationen_US
dc.subject.fieldofresearchcode010303en_US
dc.titleExtremal Optimisation for Assignment Type Problemsen_US
dc.typeBook chapteren_US
dc.type.descriptionB1 - Book Chapters (HERDC)en_US
dc.type.codeB - Book Chaptersen_US
gro.date.issued2009
gro.hasfulltextNo Full Text


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