Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2008.01.009
DC FieldValue
dc.titleCooperative co-evolution of GA-based classifiers based on input decomposition
dc.contributor.authorZhu, F.
dc.contributor.authorGuan, S.-U.
dc.date.accessioned2016-11-08T08:24:49Z
dc.date.available2016-11-08T08:24:49Z
dc.date.issued2008-12
dc.identifier.citationZhu, F., Guan, S.-U. (2008-12). Cooperative co-evolution of GA-based classifiers based on input decomposition. Engineering Applications of Artificial Intelligence 21 (8) : 1360-1369. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2008.01.009
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/129633
dc.description.abstractGenetic algorithms (GAs) have been widely used as soft computing techniques in various applications, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, a new cooperative co-evolution algorithm, namely ECCGA, is proposed in the application domain of pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution GA and normal GA. Some analysis and discussions on ECCGA and possible improvement are also presented. © 2008 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.engappai.2008.01.009
dc.sourceScopus
dc.subjectClassification
dc.subjectCooperative co-evolution
dc.subjectGenetic algorithms
dc.subjectGlobal fitness
dc.subjectInput decomposition
dc.subjectLocal fitness
dc.typeArticle
dc.contributor.departmentINSTITUTE OF SYSTEMS SCIENCE
dc.description.doi10.1016/j.engappai.2008.01.009
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume21
dc.description.issue8
dc.description.page1360-1369
dc.description.codenEAAIE
dc.identifier.isiut000261307500023
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