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• W2148303089 startingPage "14" @default.
• W2148303089 abstract "Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex nonlinear problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). This paper presents a new self-adaptive HGA (SAHGA) and compares its performance to a nonadaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on a groundwater remediation problem. Of the two hybrid algorithms, SAHGA is shown to be far more robust than NAHGA, providing fast convergence across a broad range of parameter settings. For the test problem, SAHGA needs 75% fewer function evaluations than SGA, even with an inefficient local search method. These findings demonstrate that SAHGA has substantial promise for enabling solution of larger-scale problems than was previously possible." @default.
• W2148303089 created "2016-06-24" @default.
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• W2148303089 date "2005-01-01" @default.
• W2148303089 modified "2023-09-27" @default.
• W2148303089 title "Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design" @default.
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• W2148303089 doi "https://doi.org/10.1061/(asce)0733-9496(2005)131:1(14)" @default.
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