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Type: Conference paper
Title: Selection of genetic algorithm parameters for water distribution system optimization
Author: Gibbs, M.
Dandy, G.
Maier, H.
Nixon, J.
Citation: Impacts of global climate change [electronic resource] : proceedings of the 2005 World Water and Environmental Resources Congress, May 15-19, 2005, Anchorage, Alaska / Raymond Walton (ed.) CDROM
Publisher: American Society of Civil Engineers
Issue Date: 2005
ISBN: 0784407924
Conference Name: World Water & Environmental Resources Congress (2005 : Anchorage, Alaska)
Statement of
Matthew S. Gibbs, Graeme C. Dandy, Holger R. Maier, and John B. Nixon
Abstract: The ability of Genetic Algorithm (GA) methods, to find near optimal solutions to Water Distribution System (WDS) optimization problems has been widely demonstrated. However, one of the main concerns in applying these methods is identifying suitable values for the GA parameters. The values selected for these parameters have a significant impact on the algorithm's behavior, and therefore greatly affect the quality of the final solution found, as well as the time taken to find that solution. A considerable amount of time and effort must be dedicated to the calibration of these parameters for the GA practitioner to have any confidence that the values used are producing the desired results. The impact of each parameter will be dependent on the values of the other parameters, and it is likely that there exists different combinations that will produce the same exploration/exploitation behavior. This offers the potential to reduce the number of parameters requiring calibration, thus making the task of applying these methods much simpler. This paper describes large-scale sensitivity analyses that have been used to calibrate a real coded GA with a distributed crossover operator, for a WDS optimization problem, the Cherry Hill-Brushy Plains network, ultimately leading to the identification of a new optimal solution. Through these analyses, groups of parameter values are identified that cause the algorithm to perform very well in terms of algorithm convergence and the quality of the final solutions obtained. These results demonstrate that by understanding the parameters controlling the GA, and the relationships between themthe effort required to calibrate a GA for a given application can be reduced significantly. Copyright ASCE 2005.
Description: ©2005 ASCE
DOI: 10.1061/40792(173)44
Appears in Collections:Aurora harvest
Civil and Environmental Engineering publications
Environment Institute publications

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