Evolutionary programming using a mixed mutation strategy

H Dong, Jun He, H Huang, W Hou

Research output: Contribution to journalArticle

84 Citations (Scopus)


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 languageEnglish
Pages (from-to)312-327
Number of pages16
JournalInformation Sciences
Issue number1
Publication statusPublished - 1 Jan 2007


  • global optimization
  • design of algorithms
  • evolutionary programming
  • randomized algorithms
  • mixed strategy


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