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)


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.


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


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  1. 1.
    Moscato, P.A.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Technical Report. Caltech Concurrent Computation Program Report 826, Caltech, Pasadena, CA (1989)Google Scholar
  2. 2.
    Lim, M., Yuan, Y., Omatu, S.: Efficient genetic algorithms using simple genes exchange local search policy for the quadratic assignment problem. Computational Optimization and Applications 15(3), 249–268 (2000)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Le, M.N., Ong, Y.S., Jin, Y., Sendhoff, B.: Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memetic Computing Journal 1(3), 175–190 (2009)CrossRefGoogle Scholar
  4. 4.
    Ong, Y.S., Keane, A.: Meta-Lamarckian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation 8(2), 99–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Vicini, A., Quagliarella, D.: Airfoil and wing design through hybrid optimization strategies. American Institute of Aeronautics and Astronautics Journal 37(5), 634–641 (1999)CrossRefGoogle Scholar
  6. 6.
    Houck, C., Joines, J., Kay, M.: Utilizing Lamarckian evolution and the Baldwin effect in hybrid genetic algorithms, Tech. Rep. (1996)Google Scholar
  7. 7.
    Renders, J., Bersini, H.: Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways. In: IEEE World Congress on Computational Intelligence, vol. 1, pp. 312–317 (1994)Google Scholar
  8. 8.
    Li, J., Jiao, L.C., He, W.H., Gong, M.G.: Lamarkian clone selection algorithm for CDMA multiuser detection over multi-path channels. In: Proc. of ICNN&B 2005, pp. 601–606 (2005)Google Scholar
  9. 9.
    Liang, L., Xu, G.H., Liu, D., Zhao, S.F.: Immune clonal selection optimization method with mixed mutation strategies. Bio-Inspired Computing: Theories and Applications, 37–41, September 14-17 (2007) 978-1-4244-4105-1Google Scholar
  10. 10.
    Dong, H., He, J., Huang, H., Hou, W.: Evolutionary programming using a mixed mutation strategy. Information Sciences 177(1), 312–327 (2007)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Lee, C.Y., Yao, X.: Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Transactions on Evolutionary Computation 8(1), 1–13 (2004)CrossRefGoogle Scholar
  12. 12.
    Chellapilla, K.: Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation 2(3), 91–96 (1998)CrossRefGoogle Scholar
  13. 13.
    Chen, C., Low, C.P., Yang, Z.: Preserving and exploiting genetic diversity in evolutionary programming algorithms. IEEE Transactions on Evolutionary Computation 13(3), 661–673 (2009)CrossRefGoogle Scholar
  14. 14.
    Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood based mutation operator. IEEE Trans. on Evolutionary Computation 13(3), 526–553 (2009)CrossRefGoogle Scholar
  15. 15.
    Gong, M.G., Jiao, L.C., Yang, J.: Lamarckian learning in clonal Selection Algorithm for Optimization. International Journal on Artificial Intelligence Tools 19(1), 19–37 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Chakraborty, U.K., Das, S., Konar, A.: Differential evolution with local neighborhood. In: Proc. IEEE Congr. Evol. Comput., pp. 7395–7402. IEEE Press, Piscataway (2006)Google Scholar
  17. 17.
    Hansen, N., Kern, S.: Evaluating the CMA Evolution Strategy on Multimodal Test Functions. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 282–291. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Nanyang Technol. Univ., Singapore, Tech. Report and IIT, Kanpur, India, KanGAL Report#2005005 (May 2005)Google Scholar
  19. 19.
    Powell, M.J.D.: Direct search algorithms for optimization calculations. Acta Numerica 7, 287–336 (1998)CrossRefGoogle Scholar

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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