Development of the enhanced self-adaptive hybrid genetic algorithm (e-SAHGA)

Genetic algorithms allow solution of more complex, nonlinear groundwater remediation design 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). The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. This technical note focuses on the development and application of a new HGA, the enhanced self-adaptive hybrid genetic algorithm (e-SAHGA), which is an enhancement of a previously developed HGA called SAHGA. The application of the e-SAHGA algorithm to a hypothetical groundwater remediation design problem showed 90% reliability in identifying the optimal solution faster than the SGA, with average savings of 64% across 100 random initial populations. These results are considerably improved over SAHGA, which attained only 80% reliability and 14% average savings on the same initial populations.