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dc.contributor.authorDahal, Keshav P.*
dc.contributor.authorRemde, Stephen M.*
dc.contributor.authorCowling, Peter I.*
dc.contributor.authorColledge, N.J.*
dc.date.accessioned2009-03-24T16:38:37Z
dc.date.available2009-03-24T16:38:37Z
dc.date.issued2008
dc.identifier.citationDahal KP, Remde SM, Cowling PI et al (2008) Improving metaheuristic performance by evolving a variable fitness function. In: Evolutionary computation in combinatorial optimization. 8th European Conference (EvoCOP 2008) Naples, Italy, March 26-28, 2008: 170-181.en
dc.identifier.urihttp://hdl.handle.net/10454/2498
dc.description.abstractIn this paper we study a complex real world workforce scheduling problem. We apply constructive search and variable neighbourhood search (VNS) metaheuristics and enhance these methods by using a variable fitness function. The variable fitness function (VFF) uses an evolutionary approach to evolve weights for each of the (multiple) objectives. The variable fitness function can potentially enhance any search based optimisation heuristic where multiple objectives can be defined through evolutionary changes in the search direction. We show that the VFF significantly improves performance of constructive and VNS approaches on training problems, and "learn" problem features which enhance the performance on unseen test problem instances.en
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.rights© 2008 Springer Verlag. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.comen
dc.subjectVariable fitness functionen
dc.subjectEvolutionen
dc.subjectHeuristicen
dc.subjectMeta-heuristicen
dc.subjectWorkforce scheduling problemen
dc.titleImproving metaheuristic performance by evolving a variable fitness functionen
dc.status.refereedYesen
dc.typeConference paperen
dc.type.versionAccepted manuscripten
dc.identifier.doihttps://doi.org/10.1007/978-3-540-78604-7_15
refterms.dateFOA2018-07-18T13:34:38Z


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