Mixed-Integer Evolution Strategies with Dynamic Niching

Abstract

Mixed-Integer Evolution Strategies (MIES) are a natural extension of standard Evolution Strategies (ES) for addressing optimization of various types of variables – continuous, ordinal integer, and nominal discrete – at the same time. Like most Evolutionary Algorithms (EAs), they experience problems in obtaining the global optimum in highly multimodal search landscapes. Niching methods, the extension of EAs to multimodal domains, are designed to treat this issue. In this study we present a dynamic niching technique for Mixed-Integer Evolution Strategies, based upon an existing ES niching approach, which was developed recently and successfully applied to continuous landscapes. The new approach is based on the heterogeneous distance measure that addresses search space similarity in a way consistent with the mutation operators of the MIES. We apply the proposed Dynamic Niching MIES framework to a test-bed of artificial landscapes and show the improvement on the global convergence in comparison to the standard MIES algorithm.KeywordsEvolution StrategyBarrier FunctionInteger VariableManhattan DistanceEvolution StrategyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.