Mixed-Integer Evolution Strategies with Dynamic Niching

Authors:
Rui Li;Jeroen Eggermont;Ofer M. Shir;Michael T. Emmerich;Thomas Bäck;Jouke Dijkstra;Johan H. Reiber
Affiliations:
Natural Computing Group, Leiden University, CA Leiden, The Netherlands 2300;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, RC Leiden, The Netherlands 2300;Natural Computing Group, Leiden University, CA Leiden, The Netherlands 2300;Natural Computing Group, Leiden University, CA Leiden, The Netherlands 2300;Natural Computing Group, Leiden University, CA Leiden, The Netherlands 2300;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, RC Leiden, The Netherlands 2300;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, RC Leiden, The Netherlands 2300
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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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.