Skip to main content
SpringerLink
Log in

Scaling up evolutionary programming algorithms

  • Conference paper
  • First Online:
Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

Included in the following conference series:

Abstract

Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four different EP algorithms for large-scale problems, i.e., problems whose dimension ranges from 100 to 300. The four are classical EP (CEP) [1, 2], fast EP (FEP).

This work is partially supported by a Rector's Special Research Grant from the University College, UNSW, ADFA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York, NY: John Wiley & Sons, 1966.

    Google Scholar 

  2. D. B. Fogel, System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Needham Heights, MA 02194: Ginn Press, 1991.

    Google Scholar 

  3. X. Yao and Y. Liu, “Fast evolutionary programming,” in Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming (L. J. Fogel, P. J. Angeline, and T. Bäck, eds.), (Cambridge, MA), pp. 451–460, The MIT Press, 1996.

    Google Scholar 

  4. X. Yao, G. Lin, and Y. Liu, “An analysis of evolutionary algorithms based on neighbourhood and step sizes,” in Evolutionary Programming VI: Proc. of the Sixth Annual Conference on Evolutionary Programming (P. J. Angeline, R. G. Reynolds, J. R. McDonnell, and R. Eberhart, eds.), vol. 1213 of Lecture Notes in Computer Science, (Berlin), pp. 297–307, Springer-Verlag, 1997.

    Google Scholar 

  5. X. Yao, “An overview of evolutionary computation,” Chinese Journal of Advanced Software Research (Allerton Press, Inc., New York, NY 10011), vol. 3, no. 1, pp. 12–29, 1996.

    Google Scholar 

  6. D. B. Fogel, “An introduction to simulated evolutionary optimisation,” IEEE Trans. on Neural Networks, vol. 5, no. 1, pp. 3–14, 1994.

    Google Scholar 

  7. T. Bäck and H.-P. Schwefel, “Evolutionary computation: an overview,” in Proc. of the 1996 IEEE Int'l Conf. on Evolutionary Computation (ICEC'96), Nagoya, Japan, pp. 20–29, IEEE Press, New York, NY 10017-2394, 1996.

    Google Scholar 

  8. C. Kappler, “Are evolutionary algorithms improved by large mutations?,” in Parallel Problem Solving from Nature (PPSN) IV (H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, eds.), vol. 1141 of Lecture Notes in Computer Science, (Berlin), pp. 346–355, Springer-Verlag, 1996.

    Google Scholar 

  9. N. Saravanan and D. B. Fogel, “Multi-operator evolutionary programming: a preliminary study on function optimization,” in Evolutionary Programming VI: Proc. of the Sixth Annual Conference on Evolutionary Programming (P. J. Angeline, R. G. Reynolds, J. R. McDonnell, and R. Eberhart, eds.), vol. 1213 of Lecture Notes in Computer Science, (Berlin), pp. 215–221, Springer-Verlag, 1997.

    Google Scholar 

  10. X. Yao (ed.), “Special issue on evolutionary computation,” Informatica, vol. 18, pp. 375–450, 1994.

    Google Scholar 

  11. D. B. Fogel, Evolving Artificial Intelligence. PhD thesis, University of California, San Diego, CA, 1992.

    Google Scholar 

  12. T. Bäck and H.-P. Schwefel, “An overview of evolutionary algorithms for parameter optimization,” Evolutionary Computation, vol. 1, no. 1, pp. 1–23, 1993.

    Google Scholar 

  13. W. Feller, An Introduction to Probability Theory and Its Applications, vol. 2. John Wiley & Sons, Inc., 2nd ed., 1971.

    Google Scholar 

  14. D. B. Fogel, “Empirical estimation of the computation required to discover approximate solutions to the traveling salesman problem using evolutionary programming,” in Proc. of the Second Ann. Conf. on Evol. Prog. (D. B. Fogel and W. Atmar, eds.), pp. 56–61, Evolutionary Programming Society, La Jolla, CA, 1993.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Group, School of Computer Science University College, The University of New South Wales, Australia

    Xin Yao & Yong Liu

  2. Australian Defence Force Academy, 2600, Canberra, ACT, Australia

    Xin Yao & Yong Liu

Authors
  1. Xin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yao, X., Liu, Y. (1998). Scaling up evolutionary programming algorithms. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040764

Download citation

  • DOI: https://doi.org/10.1007/BFb0040764

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation