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dc.contributor.authorDhiranuch Bunnagen_US
dc.contributor.authorMin Sunen_US
dc.date.accessioned2018-09-11T09:25:09Z-
dc.date.available2018-09-11T09:25:09Z-
dc.date.issued2005-12-01en_US
dc.identifier.issn00963003en_US
dc.identifier.other2-s2.0-28544431906en_US
dc.identifier.other10.1016/j.amc.2005.01.075en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=28544431906&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/62287-
dc.description.abstractWe present a stochastic global optimization algorithm, referred to as a Genetic Algorithm (GA), for solving constrained optimization problems over a compact search domain. It is a real-coded GA that converges in probability to the optimal solution. The constraints are treated through a repair operator. A specific repair operator is included for linear inequality constraints. © 2005 Elsevier Inc. All rights reserved.en_US
dc.subjectMathematicsen_US
dc.titleGenetic algorithm for constrained global optimization in continuous variablesen_US
dc.typeJournalen_US
article.title.sourcetitleApplied Mathematics and Computationen_US
article.volume171en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Alabamaen_US
Appears in Collections:CMUL: Journal Articles

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