Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/80914
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Simpson, A. | - |
dc.contributor.author | Dandy, G. | - |
dc.contributor.author | Murphy, L. | - |
dc.date.issued | 1994 | - |
dc.identifier.citation | Journal of Water Resources Planning and Management, 1994; 120(4):423-443 | - |
dc.identifier.issn | 0733-9496 | - |
dc.identifier.issn | 1943-5452 | - |
dc.identifier.uri | http://hdl.handle.net/2440/80914 | - |
dc.description.abstract | The 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.statementofresponsibility | Angus R. Simpson, Graeme C. Dandy and Laurence J. Murphy | - |
dc.language.iso | en | - |
dc.publisher | American Society of Civil Engineers | - |
dc.rights | Copyright © 1994 American Society of Civil Engineers | - |
dc.source.uri | http://dx.doi.org/10.1061/(asce)0733-9496(1994)120:4(423) | - |
dc.title | Genetic algorithms compared to other techniques for pipe optimization | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1061/(ASCE)0733-9496(1994)120:4(423) | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Simpson, A. [0000-0003-1633-0111] | - |
dc.identifier.orcid | Dandy, G. [0000-0001-5846-7365] | - |
Appears in Collections: | Aurora harvest 2 Civil and Environmental Engineering publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_80914.pdf | Accepted version | 1.02 MB | Adobe PDF | View/Open |
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