Abstract
A memetic algorithm with double mutation operators is proposed, termed as MADM. In this paper, the algorithm combines two meta-learning systems to improve the ability of global and local exploration. The double mutation operators in our algorithms guide the local learning operator to search the global optimum; meanwhile the main aim is to use the favorable information of each individual to reinforce the exploitation with the help of two meta-learning systems. Crossover operator and elitism selection operator are incorporated into MADM to further enhance the ability of global exploration. MADM is compared with the algorithms LCSA, DELG and CMA-ES on some benchmark problems and CEC2005’s problems. For the most problems, the experimental results demonstrate that MADM are more effective and efficient than LCSA, DELG and CMA-ES in solving numerical optimization problems.
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Li, Y., Wu, B., Jiao, L., Liu, R. (2012). Memetic Algorithm with Double Mutation for Numerical Optimization. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_9
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DOI: https://doi.org/10.1007/978-3-642-31919-8_9
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