Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1102
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dc.contributorDepartment of Electrical Engineering-
dc.creatorYang, S-
dc.creatorHo, SL-
dc.creatorNi, G-
dc.creatorMachado, JM-
dc.creatorWong, KF-
dc.date.accessioned2014-12-11T08:26:12Z-
dc.date.available2014-12-11T08:26:12Z-
dc.identifier.urihttp://hdl.handle.net/10397/1102-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectAlgorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectNumerical analysisen_US
dc.subjectOptimizationen_US
dc.subjectProbabilityen_US
dc.subjectVectorsen_US
dc.titleA new implementation of population based incremental learning method for optimization studies in electromagneticsen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: S. Y. Yangen_US
dc.description.otherinformationAuthor name used in this publication: S. L. Hoen_US
dc.description.otherinformationAuthor name used in this publication: G. Z. Nien_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractTo enhance the global search ability of Population Based Incremental Learning (PBIL) methods, it Is proposed that multiple probability vectors are to be included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE CEFC 2006 : 12th Biennial IEEE Conference of Electromagnetic Field Computation : April 30-May 3, 2006, Miami, Florida, USA, p. 162-
dcterms.issued2006-
dc.identifier.rosgroupidr26078-
dc.description.ros2005-2006 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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