完整後設資料紀錄
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dc.contributor.authorLin, Jih-Yiingen_US
dc.contributor.authorChen, Ying-pingen_US
dc.date.accessioned2014-12-08T15:28:25Z-
dc.date.available2014-12-08T15:28:25Z-
dc.date.issued2013en_US
dc.identifier.issn0020-7721en_US
dc.identifier.urihttp://hdl.handle.net/11536/20575-
dc.identifier.urihttp://dx.doi.org/10.1080/00207721.2011.577246en_US
dc.description.abstractVariable interdependency, referred to as linkage in genetic algorithms (GAs), has been among the most useful information in evolutionary optimisation. With the aid of linkage information, efficient evolution can be attained by GAs. Among variants of advanced GAs, linkages are either explicitly identified, as in perturbation-based methods, or implicitly extracted, as in estimation of distribution algorithms (EDAs). As linkage discovery can be considered a matter of information extraction, Shannon's entropy, a renowned metric, has been widely adopted in modern GAs. Despite the validation of theoretical bounds, which is not algorithm-specific, on evaluation complexity of linkage problems, a representative population sizing model for discrete EDAs has been developed based on the distribution of entropy measurement. On the other hand, though entropy metrics have been adopted in recent perturbation-based methods, relevant complexity analysis on these methods is still absent. In this article, we propose a population sizing model for a recently developed linkage identification method, called inductive linkage identification (ILI). The proposed model takes the entropy-based classification algorithm into account and is capable of providing an accurate estimation of population requirement. The adopted modelling approach is different than that for discrete EDAs and may give researchers insights into entropy-based linkage discovery approaches.en_US
dc.language.isoen_USen_US
dc.subjectinductive linkage identificationen_US
dc.subjectperturbation-based methodsen_US
dc.subjectbuilding blocksen_US
dc.subjectpopulation sizingen_US
dc.subjectdecision treesen_US
dc.subjectgenetic algorithmsen_US
dc.titlePopulation sizing for inductive linkage identificationen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00207721.2011.577246en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF SYSTEMS SCIENCEen_US
dc.citation.volume44en_US
dc.citation.issue1en_US
dc.citation.spage1en_US
dc.citation.epage13en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000311084900001-
dc.citation.woscount0-
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