Integrating adaptive mutations and family competition into genetic algorithms as function optimizer |
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Authors: | J-M Yang C-Y Kao |
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Affiliation: | (1) Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan Tel.: 886-2-2362-5336 ext 509 e-mail: {moon, cykao}@csie.ntu.edu.tw, TW |
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Abstract: | In this paper, we propose a robust evolutionary algorithm, called adaptive mutations genetic algorithm, for function optimization
problems. Our main contribution is robustly optimizing problems whose number of variables from 2 to 200. In order to have
a fair comparison, we propose the criteria for constructing a testing bed and for classifying these problems into different
complexity degrees. The proposed approach, based on the family competition and multiple adaptive rules, successfully integrates
the decreasing-based Gaussian mutation and self-adaptive Cauchy mutation to balance the exploitation and exploration. It is
implemented and applied to widely used test functions and several nonseparable multimodal functions. Experimental results
indicate that our approach is more robust than ten evolutionary algorithms. |
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Keywords: | Function optimization Family competition Self-adaptive mutation Genetic algorithms Cauchy mutation |
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