Skip to main content
SpringerLink
Log in
Book cover

European Conference on Genetic Programming

EuroGP 2000: Genetic Programming pp 259–270Cite as

A Metric for Genetic Programs and Fitness Sharing

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1802))

Abstract

In the paper a metric for genetic programs is constructed. This metric reflects the structural difference of the genetic programs. It is used then for applying fitness sharing to genetic programs, in analogy with fitness sharing applied to genetic algorithms. The experimental results for several parameter settings are discussed. We observe that by applying fitness sharing the code growth of genetic programs could be limited.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

  2. Bohnebeck, U., Horvath, T., Wrobel, S.: Term comparisons in first-order similarity measures. In: Page, D.L. (ed.) ILP 1998. LNCS (LNAI), vol. 1446, pp. 65–79. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  3. Giles, J.R.: Introduction to the Analysis of Metric Spaces. Australian Mathematical Society Lecture Series (1987)

    Google Scholar 

  4. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  5. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  6. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82–87 (1994)

    Google Scholar 

  7. Iba, H., de Garis, H., Sato, T.: Genetic programming using a minimum description length principle. In: Kinnear, K.E. (ed.) Advances in Genetic Programming, pp. 265–284. MIT Press, Cambridge (1994)

    Google Scholar 

  8. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  9. Lu, S.-Y.: The tree-to-tree distance and its application to cluster analysis. IEEE Transactions on PAMI 1(2), 219–224 (1979)

    MATH  Google Scholar 

  10. Mahfoud, S.: Niching methods for genetic algorithms, Illigal report 95001, University of Illinois at Urbana-Champaign (1995)

    Google Scholar 

  11. Nienhuys-Cheng, S.-H.: Distance between Herbrand interpretations: a measure for approximations to a target concept. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS (LNAI), vol. 1297, pp. 213–226. Springer, Heidelberg (1997)

    Google Scholar 

  12. Oei, C.K., Goldberg, D.E., Chang, S.J.: Tournament selection, niching and the preservation of diversity, Illigal report 91011, University of Illinois at Urbana-Champaign (1991)

    Google Scholar 

  13. Selkow, S.M.: The tree-to-tree editing problem. Information Processing Letters 6(6), 184–186 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  14. Soule, T., Foster, J.A., Dickinson, J.: Code growth in genetic programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 215–223 (1996)

    Google Scholar 

  15. Tai, K.-C.: The tree-to-tree correction problem. Journal of the ACM 26(3), 422–433 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  16. Yin, X., Germay, N.: A fast genetic algorithm with sharing scheme using cluster analysis methods in multimodal function optimization. In: Albrecht, R.F., Steele, N.C., Reeves, C.R. (eds.) Artificial Neural Nets and Genetic Algorithms, pp. 450–457 (1993)

    Google Scholar 

  17. Zhang, B.-T., Muhlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)

    Article  Google Scholar 

  18. Zhang, K., Statman, R., Shasha, D.: On the editing distance between unordered labeled trees. Information Processing Letters 42, 133–139 (1992)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer and Automation Research Institute, Hungarian Academy of Sciences, 1518, Budapest, POB. 63, Hungary

    Anikó Ekárt & S. Z. Németh

Authors
  1. Anikó Ekárt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Z. Németh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Riccardo Poli

  2. Department of Computer Science, Memorial University of Newfoundland, Canada

    Wolfgang Banzhaf

  3. Departments of Mathematical, Biological Sciences and Computing and Electronic Systems, University of Essex, UK

    William B. Langdon

  4. School of Computer Science, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Julian Miller

  5. Dept. of Physical Rescource Theory, Chalmers University of Technology, 412 96, Göteborg, Sweden

    Peter Nordin

  6. School of Computing, Information Systems and Mathematics, South Bank University, SE1 0AA, London, UK

    Terence C. Fogarty

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ekárt, A., Németh, S.Z. (2000). A Metric for Genetic Programs and Fitness Sharing. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-46239-2_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67339-2

  • Online ISBN: 978-3-540-46239-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation