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dc.contributor.authorÖzdamar, Linet
dc.contributor.authorDemirhan, M
dc.date.accessioned2014-11-05T15:12:30Z
dc.date.available2014-11-05T15:12:30Z
dc.date.issued2001-01-01
dc.identifier.issn0165-0114
dc.identifier.urihttp://hdl.handle.net/11413/823
dc.description.abstractAn adaptive partitioning algorithm with random search is proposed to locate the global optimum of multimodal functions. Partitioning algorithms divide the feasible region into nonoverlapping partitions in order to restrict and direct the search to the most promising region expected to contain the global optimum. In such a scheme a partition evaluation measure is required to assess sub-regions in order to re-partition the most promising sub-region and intensify the search within that area. This study provides computational results on several classes of partition evaluation measures used in the assessment of samples taken from all partitions. Among the partition evaluation classes used in our comparison are fuzzy, statistical, and deterministic interval estimation measures. Performance in terms of solution quality is reported on an extensive set of 77 test functions collected from the literature.tr_TR
dc.language.isoentr_TR
dc.publisherELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDStr_TR
dc.relationFUZZY SETS AND SYSTEMStr_TR
dc.subjectglobal optimizationtr_TR
dc.subjectadaptive partitioning algorithmstr_TR
dc.subjectmeasure of fuzzinesstr_TR
dc.subjectentropytr_TR
dc.subjectglobal optimizasyontr_TR
dc.subjectadaptif bölümleme algoritmalarıtr_TR
dc.subjectBulanıklık ölçüsütr_TR
dc.subjectentropitr_TR
dc.titleComparison of Partition Evaluation Measures in an Adaptive Partitioning Algorithm for Global Optimizationtr_TR
dc.typeArticletr_TR


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