Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/80914
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dc.contributor.authorSimpson, A.-
dc.contributor.authorDandy, G.-
dc.contributor.authorMurphy, L.-
dc.date.issued1994-
dc.identifier.citationJournal of Water Resources Planning and Management, 1994; 120(4):423-443-
dc.identifier.issn0733-9496-
dc.identifier.issn1943-5452-
dc.identifier.urihttp://hdl.handle.net/2440/80914-
dc.description.abstractThe genetic algorithm technique is a relatively new optimization technique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization techniques to a case study pipe network. The genetic algorithm technique finds the global optimum in relatively few evaluations compared to the size of the search space.-
dc.description.statementofresponsibilityAngus R. Simpson, Graeme C. Dandy and Laurence J. Murphy-
dc.language.isoen-
dc.publisherAmerican Society of Civil Engineers-
dc.rightsCopyright © 1994 American Society of Civil Engineers-
dc.titleGenetic algorithms compared to other techniques for pipe optimization-
dc.typeJournal article-
dc.identifier.doi10.1061/(ASCE)0733-9496(1994)120:4(423)-
pubs.publication-statusPublished-
dc.identifier.orcidSimpson, A. [0000-0003-1633-0111]-
dc.identifier.orcidDandy, G. [0000-0001-5846-7365]-
Appears in Collections:Aurora harvest 2
Civil and Environmental Engineering publications

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