Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6949
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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorChan, CY-
dc.creatorXue, F-
dc.creatorIp, WH-
dc.creatorCheung, CF-
dc.date.accessioned2014-12-11T08:29:17Z-
dc.date.available2014-12-11T08:29:17Z-
dc.identifier.isbn978-3-642-34412-1 (print)-
dc.identifier.isbn978-3-642-34413-8 (online)-
dc.identifier.urihttp://hdl.handle.net/10397/6949-
dc.language.isoenen_US
dc.publisherSpringer Berlin / Heidelbergen_US
dc.rights© Springer-Verlag Berlin Heidelberg 2012. The final publication is available on http://link.springer.com.en_US
dc.subjectOptimizationen_US
dc.subjectShellfishen_US
dc.titleA hyper-heuristic inspired by pearl huntingen_US
dc.typeBook Chapteren_US
dc.identifier.spage349-
dc.identifier.epage353-
dc.identifier.doi10.1007/978-3-642-34413-8_26-
dcterms.abstractPearl hunting is a traditional way of diving to retrieve pearl from pearl oysters or to hunt some other sea creatures. In some areas, hunters need to dive and search seafloor repeatedly at several meters depth for pearl oysters. In a search perspective, pearl hunting consists of repeated diversification (to surface and change target area) and intensification (to dive and find pearl oysters). A Pearl Hunter (PHunter) hyper-heuristic is inspired by the pearl hunting, as shown in Fig. 1. Given a problem domain and some low-level heuristics (LLHs), PHunter can group, test, select and organize LLHs for the domain by imitating a rational diver.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn Y Hamadi & M Schoenauer (Eds.), Learning & intelligent optimization, p. 349-353. Berlin ; New York: Springer, 2012-
dcterms.issued2012-
dc.identifier.scopus2-s2.0-84867872173-
dc.relation.ispartofbookLearning & intelligent optimization-
dc.publisher.placeBerlin ; New Yorken_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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