Designing a new elitist Nondominated Sorted Genetic Algorithm for a multiobjective long term groundwater monitoring application

This study presents a niching-based elitist enhancement of the Non-dominated Sorted Genetic Algorithm (NSGA) and tests its performance in identifying the Pareto frontier for a groundwater monitoring application. The application utilizes historical data at a single snapshot in time to identify potential spatial redundancies within a monitoring network. The study combines nonlinear spatial interpolation with the elitist NSGA to identify the Pareto frontier for sampling costs and local concentration estimation errors. The Elitist NSGA nearly replicated the true front, finding representative solutions along the entire trade off between cost and estimation error.

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