A Niched Pareto Genetic Algorithm for Multiobjective Optimization

| Many, if not most, optimization problems have multiple objectives. Historically , multiple objectives have been combined ad hoc to form a scalar objective function , usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modiied to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoo surface. We introduce the Niched Pareto GA as an algorithm for nding the Pareto optimal set. We demonstrate its ability to nd and maintain a diverse \Pareto optimal popula-tion" on two artiicial problems and an open problem in hydrosystems.