Multiobjective evolutionary strategy for finding neighbourhoods of pareto-optimal solutions

In some cases of Multiobjective Optimization problems finding Pareto optimal solutions does not give enough knowledge about the shape of the landscape, especially with multimodal problems and non-connected Pareto fronts. In this paper we present a strategy which combines a hierarchic genetic algorithm consisting of multiple populations with rank selection. This strategy aims at finding neighbourhoods of solutions by recognizing regions with high density of individuals. We compare two variants of the presented strategy on a benchmark two-criteria minimization problem.

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