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Multi-operator evolutionary programming: A preliminary study on function optimization

  • N. Saravanan
  • David B. Fogel
Enhanced Evolutionary Operators
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1213)

Abstract

Classical evolutionary programming uses Gaussian mutation as the primary search operator. Recent studies have shown that using a Cauchy random variable as the primary operator leads to faster convergence for certain function optimization problems. In this study we explore the use of both the Gaussian and the Cauchy operators along with a self-adaptive mechanism to select the appropriate operator for each individual in the population. Empirical studies of the dual-operator evolutionary programming are conducted using a limited set of test function optimization problems.

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References

  1. T. Bäck and H.-P. Schwefel (1993) “An overview of evolutionary algorithms for parameter optimization,” Evolutionary Computation, Vol. 1, No. 1, pp. 1–24.Google Scholar
  2. D.B. Fogel (1995) Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, IEEE Press, NYGoogle Scholar
  3. N. Saravanan, D.B. Fogel, and K.M. Nelson (1995) “A Comparison of Methods for Self-Adaptation in Evolutionary Algorithms,” BioSystems, Vol. 36, pp. 157–166.CrossRefPubMedGoogle Scholar
  4. X. Yao and Y. Liu (1996) “Fast Evolutionary Programming,” Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L.J. Fogel, P.J. Angeline, and T. Back (eds.), MIT Press, Cambridge, MA, pp. 451–460.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • N. Saravanan
    • 1
  • David B. Fogel
    • 2
  1. 1.ETA, Inc.Madison Heights
  2. 2.Natural Selection, Inc.La Jolla

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