A robust real-coded evolutionary algorithm with toroidal search space conversion

Authors:
H. Someya;M. Yamamura
Affiliations:
The Institute of Statistical Mathematics, Department of Prediction and Control, 4-6-7 Minami-Azabu Minato-ku, 106-8569, Tokyo, Japan;Tokyo Institute of Technology, Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, 4259 Nagatsuta Midori-ku, 226-8502, Yokoh ...
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2005

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Abstract

This paper presents a robust Real-coded evolutionary algorithm. Real-coded evolutionary algorithms (RCEAs), such as real-coded genetic algorithms and evolution strategies, are known as effective methods for function optimization. However, they often fail to find the optimum in case the objective function is multimodal, discrete or high-dimensional. It is also reported that most crossover (or recombination) operators for RCEAs has sampling bias that prevents to find the optimum near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of search space. In this paper, we apply two methods, genetic algorithm with search area adaptation (GSA) and toroidal search space conversion (TSC), to the function optimization for improving the robustness of RCEAs. The former method searches adaptively and the latter one removes the sampling bias. Through several experiments, we have confirmed that GSA works adaptively and it shows higher performance, and RCEAs with TSC show effectiveness to find the optima near the boundary of search space.