Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/6949
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | - |
dc.creator | Chan, CY | - |
dc.creator | Xue, F | - |
dc.creator | Ip, WH | - |
dc.creator | Cheung, CF | - |
dc.date.accessioned | 2014-12-11T08:29:17Z | - |
dc.date.available | 2014-12-11T08:29:17Z | - |
dc.identifier.isbn | 978-3-642-34412-1 (print) | - |
dc.identifier.isbn | 978-3-642-34413-8 (online) | - |
dc.identifier.uri | http://hdl.handle.net/10397/6949 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer Berlin / Heidelberg | en_US |
dc.rights | © Springer-Verlag Berlin Heidelberg 2012. The final publication is available on http://link.springer.com. | en_US |
dc.subject | Optimization | en_US |
dc.subject | Shellfish | en_US |
dc.title | A hyper-heuristic inspired by pearl hunting | en_US |
dc.type | Book Chapter | en_US |
dc.identifier.spage | 349 | - |
dc.identifier.epage | 353 | - |
dc.identifier.doi | 10.1007/978-3-642-34413-8_26 | - |
dcterms.abstract | Pearl 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Y Hamadi & M Schoenauer (Eds.), Learning & intelligent optimization, p. 349-353. Berlin ; New York: Springer, 2012 | - |
dcterms.issued | 2012 | - |
dc.identifier.scopus | 2-s2.0-84867872173 | - |
dc.relation.ispartofbook | Learning & intelligent optimization | - |
dc.publisher.place | Berlin ; New York | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Book Chapter |
Files in This Item:
File | Description | Size | Format | |
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Chan_a_hyper_heuristic.pdf | Pre-published version | 82.94 kB | Adobe PDF | View/Open |
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