Advertisement

Tracking extrema in dynamic environments

  • Peter J. Angeline
Issues in Adaptability: Theory and practice
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1213)

Abstract

Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Angeline, P. J. (1995). Adaptive and Self-Adaptive Evolutionary Computations. In Computational Intelligence: A Dynamic System Perspective, M. Palaniswami, Y. Attikiouzel, R. Marks, D. Fogel and T. Fukuda (eds.), Piscataway, NJ: IEEE Press, pp. 152–163.Google Scholar
  2. 2.
    Angeline, P. J., Fogel, D. B. and Fogel, L. J. (1996). A Comparison of Self-Adaptive Update Rules for Finite State Machines in Dynamic Environments. In Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L. Fogel, P. Angeline and T. Bäck (eds.). Cambridge, MA: MIT Press, pp. 441–450.Google Scholar
  3. 3.
    Bäck, T. and Schwefel, H.-P. (1993). An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation, 1 (1), pp. 1–24.Google Scholar
  4. 4.
    Fogel, D.B. (1995). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Piscataway, NJ: IEEE Press.Google Scholar
  5. 5.
    Fogel, D.B., Fogel, L.J. and Atmar, J.W. (1991). Meta-Evolutionary Programming. Proc. of the 25th Asilomar Conference on Signals, Systems and Computers, R.R. Chen (ed.), San Jose, CA: Maple Press, pp. 540–545.Google Scholar
  6. 6.
    Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966). Artificial Intelligence through Simulated Evolution, New York: John Wiley & Sons.Google Scholar
  7. 7.
    Fogel, L.J., Angeline, P.J. and Fogel, D.B. (1995). An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines. In Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming, J. McDonnell, R. Reynolds, and D. Fogel (eds), Cambridge, MA: MIT Press, pp. 355–366.Google Scholar
  8. 8.
    Saravanan, N., Fogel, D. B. and Nelson, K. M. (1995) A Comparison of Methods for Self-Adaptation in Evolutionary Algorithms. BioSystems, 36, pp. 157–166.CrossRefPubMedGoogle Scholar
  9. 9.
    Schwefel, H.-P. (1995). Evolution and Optimum Seeking. New York: John Wiley & Sons.Google Scholar
  10. 10.
    Yao, X. and Liu, Y. (1996). Fast Evolutionary Programming, In Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L. Fogel, P. Angeline and T. Bäck (eds.). Cambridge, MA: MIT Press, pp. 451–460.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Peter J. Angeline
    • 1
  1. 1.Lockheed Martin Federal SystemsOwego

Personalised recommendations