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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, Levy, 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. (c) 2006 Elsevier Inc. All rights reserved.
Original language | English |
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Pages (from-to) | 312-327 |
Number of pages | 16 |
Journal | Information Sciences |
Volume | 177 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2007 |
Keywords
- global optimization
- design of algorithms
- evolutionary programming
- randomized algorithms
- mixed strategy
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Dive into the research topics of 'Evolutionary programming using a mixed mutation strategy'. Together they form a unique fingerprint.Projects
- 1 Finished
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Market Based Control of Complex Computational Systems
Engineering & Physical Science Research Council
1/10/04 → 31/03/10
Project: Research Councils