Designing A New Elitist Nondominated Sorted Genetic Algorithm For A Multiobjective Long Term Groundwater Monitoring Application
نویسندگان
چکیده
Although usage of genetic algorithms (GAs) has become widespread, the theoretical work from the genetic and evolutionary computation (GEC) field has been largely ignored by practitioners in realworld applications. This paper provides an overview of a three-step method for utilizing GEC theory to ensure robust search and avoid the common pitfalls in GA applications. Additionally, this study presents a niching-based elitist enhancement of the Nondominated Sorted Genetic Algorithm (NSGA) and tests its performance in identifying the Pareto frontier for a groundwater monitoring application. 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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