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dc.contributor.authorWong, Kachun
dc.contributor.authorWu, Chunho
dc.contributor.authorMok, Ricky
dc.contributor.authorPeng, Chengbin
dc.contributor.authorZhang, Zhaolei
dc.date.accessioned2015-08-03T09:57:22Z
dc.date.available2015-08-03T09:57:22Z
dc.date.issued2012-07
dc.identifier.citationWong, K.-C., Wu, C.-H., Mok, R. K. P., Peng, C., & Zhang, Z. (2012). Evolutionary multimodal optimization using the principle of locality. Information Sciences, 194, 138–170. doi:10.1016/j.ins.2011.12.016
dc.identifier.issn00200255
dc.identifier.doi10.1016/j.ins.2011.12.016
dc.identifier.urihttp://hdl.handle.net/10754/562233
dc.description.abstractThe principle of locality is one of the most widely used concepts in designing computing systems. To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization. Instead of generating trial vectors randomly, the first method proposed takes advantage of spatial locality to generate trial vectors. Temporal locality is also adopted to help generate offspring in the second method proposed. Temporal and spatial locality are then applied together in the third method proposed. Numerical experiments are conducted to compare the proposed methods with the state-of-the-art methods on benchmark functions. Experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality. Further experiments are also conducted on two application problems. One is the varied-line-spacing holographic grating design problem, while the other is the protein structure prediction problem. The numerical results demonstrate the effectiveness of the methods proposed. © 2012 Elsevier Inc. All rights reserved.
dc.description.sponsorshipThe authors would like to express their deep gratitudes to the anonymous reviewers for their constructive comments. The authors would also like to thank Ling Qing for his source codes and insightful discussions. Last but not least, the authors would like to thank Kwong-Sak Leung and Man-Hon Wong for their contributions. ZZ acknowledges funding support from a NSERC Discovery Grant (RGPIN 327612-09).
dc.publisherElsevier BV
dc.subjectCrowding differential evolution
dc.subjectEvolutionary Computation
dc.subjectMultimodal optimization
dc.subjectNiching
dc.subjectOptimization
dc.subjectSpatial locality
dc.subjectTemporal locality
dc.titleEvolutionary multimodal optimization using the principle of locality
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalInformation Sciences
dc.contributor.institutionDepartment of Computer Science, University of Toronto, Toronto, ON, Canada
dc.contributor.institutionTerrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
dc.contributor.institutionDepartment of ISE, Hong Kong Polytechnic University, Hung Hom, Hong Kong
dc.contributor.institutionDepartment of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong
dc.contributor.institutionBanting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada
dc.contributor.institutionDepartment of Molecular Genetics, University of Toronto, Toronto, ON, Canada
kaust.personPeng, Chengbin


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