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Associative Memory Scheme for Genetic Algorithms in Dynamic Environments

  • Shengxiang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

In recent years dynamic optimization problems have attracted a growing interest from the community of genetic algorithms with several approaches developed to address these problems, of which the memory scheme is a major one. In this paper an associative memory scheme is proposed for genetic algorithms to enhance their performance in dynamic environments. In this memory scheme, the environmental information is also stored and associated with current best individual of the population in the memory. When the environment changes the stored environmental information that is associated with the best re-evaluated memory solution is extracted to create new individuals into the population. Based on a series of systematically constructed dynamic test environments, experiments are carried out to validate the proposed associative memory scheme. The environmental results show the efficiency of the associative memory scheme for genetic algorithms in dynamic environments.

Keywords

Genetic Algorithm Dynamic Environment Memory Scheme Dynamic Optimization Problem Environmental Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Tech. Report CMUCS- 94-163, Carnegie Mellon University (1994)Google Scholar
  2. 2.
    Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proc. of the 1999 Congr. on Evol. Comput., vol. 3, pp. 1875–1882 (1999)Google Scholar
  3. 3.
    Branke, J., Kaußler, T., Schmidth, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Proc. of the Adaptive Computing in Design and Manufacturing, pp. 299–308 (2000)Google Scholar
  4. 4.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)MATHGoogle Scholar
  5. 5.
    Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. In: Proc. of the 5th Int. Conf. on Genetic Algorithms, pp. 523–530 (1993)Google Scholar
  6. 6.
    Goldberg, D.E., Smith, R.E.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Proc. of the 2nd Int. Conf. on Genetic Algorithms, pp. 59–68 (1987)Google Scholar
  7. 7.
    Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proc. of the 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137–144 (1992)Google Scholar
  8. 8.
    Karaman, A., Uyar, S., Eryigit, G.: The memory indexing evolutionary algorithm for dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 563–573. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Lewis, E.H.J., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 139–148. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Mori, N., Kita, H., Nishikawa, Y.: Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm. In: Proc. of the 7th Int. Conf. on Genetic Algorithms, pp. 299–306 (1997)Google Scholar
  11. 11.
    Morrison, R.W., De Jong, K.A.: Triggered hypermutation revisited. In: Proc. of the 2000 Congress on Evol. Comput., pp. 1025–1032 (2000)Google Scholar
  12. 12.
    Ng, K.P., Wong, K.C.: A new diploid scheme and dominance change mechanism for non-stationary function optimisation. In: Proc. of the 6th Int. Conf. on Genetic Algorithms (1997)Google Scholar
  13. 13.
    Ramsey, C.L., Greffenstette, J.J.: Case-based initializtion of genetic algorithms. In: Proc. of the 5th Int. Conf. on Genetic Algorithms (1993)Google Scholar
  14. 14.
    Simões, A., Costa, E.: An immune system-based genetic algorithm to deal with dynamic environments: diversity and memory. In: Proc. of the 6th Int. Conf. on Neural Networks and Genetic Algorithms, pp. 168–174 (2003)Google Scholar
  15. 15.
    Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proc. of the 1999 Congress on Evol. Comput., pp. 1843–1850 (1999)Google Scholar
  16. 16.
    Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proc. of the 2003 IEEE Congress on Evol. Comput., vol. 3, pp. 2246–2253 (2003)Google Scholar
  17. 17.
    Yang, S.: Population-based incremental learning with memory scheme for changing environments. In: Proc. of the 2005 Genetic and Evol. Comput. Conference, vol. 1, pp. 711–718 (2005)Google Scholar
  18. 18.
    Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing 9(11), 815–834 (2005)MATHCrossRefGoogle Scholar
  19. 19.
    Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. Submitted to IEEE Trans. on Evol. Comput. (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shengxiang Yang
    • 1
  1. 1.Department of Computer ScienceUniversity of LeicesterLeicesterUnited Kingdom

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