Skip to main content
SpringerLink
Log in

Convergence of Program Fitness Landscapes

  • Conference paper
  • First Online:
Book cover Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

Included in the following conference series:

Abstract

Point mutation has no effect on almost all linear programs. In two genetic programming (GP) computers (cyclic and bit flip) we calculate the fitness evaluations needed using steepest ascent and first ascent hill climbers and evolutionary search. We describe how the average fitness landscape scales with program length and give general bounds.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Banzhaf et al., 1998]_Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, and Frank D. Francone. Genetic Programming — An Introduction. Morgan Kaufmann.

    Google Scholar 

  2. [Clergue et al., 2002]_Fitness distance correlation and problem difficulty for genetic programming. In GECCO 2002, pp 724–732, New York, 9–13 July 2002.

    Google Scholar 

  3. [Daida et al., 2001]_Jason M. Daida, et al.. What makes a problem GP-hard? Genetic Programming and Evolvable Machines, 2(2):165–191, June 2001.

    Article  MATH  Google Scholar 

  4. A comparative analysis of selection schemes used in genetic algorithms. In G. J. E. Rawlins, editor, FOGA, pp 69–93. Morgan Kaufmann.

    Google Scholar 

  5. Terry Jones. One operator, one landscape. Technical Report SFI TR 95-02-025, Santa Fe Institute, January 1995.

    Google Scholar 

  6. Kenneth E. Kinnear, Jr. Fitness landscapes and difficulty in genetic programming. In WCCI, pp 142–147, Orlando, 27–29 June 1994. IEEE Press.

    Google Scholar 

  7. W. B. Langdon and Riccardo Poli. Foundations of Genetic Programming. Springer-Verlag, 2002.

    Google Scholar 

  8. Convergence rates for the distribution of program outputs. In GECCO 2002, pp 812–819, New York, 9–13 July 2002. Morgan Kaufmann.

    Google Scholar 

  9. W. B. Langdon. How many good programs are there? How long are they? In Jonathan Rowe, et al. editors, FOGA VII. Morgan Kaufmann.

    Google Scholar 

  10. W. B. Langdon. The distribution of reversible functions is Normal. In Rick Riolo, editor, GP Theory and Practise. 2003. Forthcoming.

    Google Scholar 

  11. Predictive models for the breeder genetic algorithm. Evolutionary Computation, 1(1):25–49, 1993.

    Google Scholar 

  12. Concepts of inductive genetic programming. In W. Banzhaf, et al. editors, EuroGP, LNCS 1391, pp 49–60. Springer-Verlag.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science, University College, London, Gower Street, London, UK

    W. B. Langdon

Authors
  1. W. B. Langdon
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Langdon, W.B. (2003). Convergence of Program Fitness Landscapes. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_63

Download citation

  • DOI: https://doi.org/10.1007/3-540-45110-2_63

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation