Evolutionary programming using a mixed mutation strategy |
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Authors: | Hongbin Dong Jun He Houkuan Huang |
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Affiliation: | a School of Computer Science and Information Technology, Beijing Jiaotong University, Beijing 100044, China b Department of Computer Science, Harbin Normal University, Harbin 150080, China c School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom |
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Abstract: | Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorithm that employs the Gaussian, Cauchy, Lévy, and single-point mutation operators. The novel algorithm is tested on a set of 22 benchmark problems. The results show that the mixed strategy performs equally well or better than the best of the four pure strategies does, for all of the benchmark problems. |
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Keywords: | Design of algorithms Randomized algorithms Global optimization Evolutionary programming Mixed strategy |
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