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dc.contributor.authorChen, Ying-pingen_US
dc.contributor.authorChen, Chao-Hongen_US
dc.date.accessioned2014-12-08T15:06:49Z-
dc.date.available2014-12-08T15:06:49Z-
dc.date.issued2010-06-01en_US
dc.identifier.issn1063-6560en_US
dc.identifier.urihttp://dx.doi.org/10.1162/evco.2010.18.2.18202en_US
dc.identifier.urihttp://hdl.handle.net/11536/5353-
dc.description.abstractAn adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.en_US
dc.language.isoen_USen_US
dc.subjectEstimation of distribution algorithmen_US
dc.subjectEDAen_US
dc.subjectECGAen_US
dc.subjectsplit-on-demanden_US
dc.subjectSoDen_US
dc.subjectreal-parameter optimizationen_US
dc.subjecteconomic dispatchen_US
dc.subjectvalve point effecten_US
dc.titleEnabling the Extended Compact Genetic Algorithm for Real-Parameter Optimization by Using Adaptive Discretizationen_US
dc.typeArticleen_US
dc.identifier.doi10.1162/evco.2010.18.2.18202en_US
dc.identifier.journalEVOLUTIONARY COMPUTATIONen_US
dc.citation.volume18en_US
dc.citation.issue2en_US
dc.citation.spage199en_US
dc.citation.epage228en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000277101800002-
dc.citation.woscount5-
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