TY - GEN
T1 - Evaluation of advanced genetic algorithms applied to groundwater remediation design
AU - Hayes, Marcia S.
AU - Minsker, Barbara S.
PY - 2005
Y1 - 2005
N2 - Optimal design of a groundwater pump and treat system is a difficult task, especially given the computationally intensive nature of field-scale remediation design. Genetic algorithms (GAs) have been used extensively for remediation design because of their flexibility and global search capabilities, but computational intensity is a particularly difficult issue with GAs. This paper discusses a new competent GA, the hierarchical Bayesian Optimization Algorithm (hBOA), which is designed to reduce the computational effort. GAs operate by assembling highly fit segments of chromosomes (potential solutions), called building blocks. The hBOA enhances the efficiency of this process by using a Bayesian network to create models of the building blocks. The building blocks are nodes on the network, and the algorithm uses the network to generate new solutions, retaining the best building blocks of the parents. This work compares the performance of hBOA to a simple genetic algorithm (SGA) in solving a case study to determine if any benefit can be gained through the use of this approach. This work demonstrates that hBOA more reliably identifies the optimal solution to this groundwater remediation design problem. Copyright ASCE 2005.
AB - Optimal design of a groundwater pump and treat system is a difficult task, especially given the computationally intensive nature of field-scale remediation design. Genetic algorithms (GAs) have been used extensively for remediation design because of their flexibility and global search capabilities, but computational intensity is a particularly difficult issue with GAs. This paper discusses a new competent GA, the hierarchical Bayesian Optimization Algorithm (hBOA), which is designed to reduce the computational effort. GAs operate by assembling highly fit segments of chromosomes (potential solutions), called building blocks. The hBOA enhances the efficiency of this process by using a Bayesian network to create models of the building blocks. The building blocks are nodes on the network, and the algorithm uses the network to generate new solutions, retaining the best building blocks of the parents. This work compares the performance of hBOA to a simple genetic algorithm (SGA) in solving a case study to determine if any benefit can be gained through the use of this approach. This work demonstrates that hBOA more reliably identifies the optimal solution to this groundwater remediation design problem. Copyright ASCE 2005.
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U2 - 10.1061/40792(173)339
DO - 10.1061/40792(173)339
M3 - Conference contribution
AN - SCOPUS:37249040975
SN - 0784407924
SN - 9780784407929
T3 - World Water Congress 2005: Impacts of Global Climate Change - Proceedings of the 2005 World Water and Environmental Resources Congress
BT - World Water Congress 2005
T2 - 2005 World Water and Environmental Resources Congress
Y2 - 15 May 2005 through 19 May 2005
ER -