Memetic algorithm with double mutation for numerical optimization

Authors:
Yangyang Li;Bo Wu;Lc Jiao;Ruochen Liu
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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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.