Memetic Algorithm with Double Mutation for Numerical Optimization

  • Yangyang Li
  • Bo Wu
  • Lc Jiao
  • Ruochen Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7202)

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.

Keywords

Memetic algorithm double mutation operator two meta-learning systems numerical optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yangyang Li
    • 1
  • Bo Wu
    • 1
  • Lc Jiao
    • 1
  • Ruochen Liu
    • 1
  1. 1.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of ChinaXidian UniversityXi’anChina

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