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Article: An extensible genetic algorithm framework for problem solving in a common environment

TitleAn extensible genetic algorithm framework for problem solving in a common environment
Authors
Chuang, ASWu, F
KeywordsGenetic algorithms
Graphical user interfaces
Object-oriented programming
Optimization methods
Issue Date1999
PublisherI E E E. The Journal's web site is located at http://www.ieee.org
Citation
Ieee Power Engineering Review, 1999, v. 19 n. 2, p. 47 How to Cite?
DOI: http://dx.doi.org/10.1109/59.852132
AbstractAn object-oriented framework is described for solving mathematical programs using genetic algorithms (GA). The advantages of this framework are its extensibility, modular design, and accessibility to existing programming code. The framework also incorporates a graphical user's interface that may be used to build new GA's as well as run GA simulations. Two power system problems are solved by implementing genetic algorithms using the framework. The first is a continuous optimization problem and the second an integer programming problem. We illustrate the flexibility of the framework as well as its other features on our test problems.
Persistent Identifierhttp://hdl.handle.net/10722/42833
ISSN
0272-1724
ISI Accession Number ID
WOS:000087916000040

 

DC FieldValueLanguage
dc.contributor.authorChuang, ASen_HK
dc.contributor.authorWu, Fen_HK
dc.date.accessioned2007-03-23T04:33:03Z-
dc.date.available2007-03-23T04:33:03Z-
dc.date.issued1999en_HK
dc.identifier.citationIeee Power Engineering Review, 1999, v. 19 n. 2, p. 47en_HK
dc.identifier.issn0272-1724en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42833-
dc.description.abstractAn object-oriented framework is described for solving mathematical programs using genetic algorithms (GA). The advantages of this framework are its extensibility, modular design, and accessibility to existing programming code. The framework also incorporates a graphical user's interface that may be used to build new GA's as well as run GA simulations. Two power system problems are solved by implementing genetic algorithms using the framework. The first is a continuous optimization problem and the second an integer programming problem. We illustrate the flexibility of the framework as well as its other features on our test problems.en_HK
dc.format.extent955135 bytes-
dc.format.extent28160 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.ieee.orgen_HK
dc.relation.ispartofIEEE Power Engineering Reviewen_HK
dc.rights©2000 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.-
dc.subjectGenetic algorithmsen_HK
dc.subjectGraphical user interfacesen_HK
dc.subjectObject-oriented programmingen_HK
dc.subjectOptimization methodsen_HK
dc.titleAn extensible genetic algorithm framework for problem solving in a common environmenten_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0885-8950&volume=15&issue=1&spage=269&epage=275&date=2000&atitle=An+extensible+genetic+algorithm+framework+for+problem+solving+in+a+common+environmenten_HK
dc.identifier.emailWu, F: ffwu@eee.hku.hken_HK
dc.identifier.authorityWu, F=rp00194en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/59.852132en_HK
dc.identifier.scopuseid_2-s2.0-33747377874en_HK
dc.identifier.hkuros51353-
dc.identifier.volume19en_HK
dc.identifier.issue2en_HK
dc.identifier.spage47en_HK
dc.identifier.epage47en_HK
dc.identifier.isiWOS:000087916000040-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChuang, AS=7006267881en_HK
dc.identifier.scopusauthoridWu, F=7403465107en_HK
dc.identifier.issnl0272-1724-

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