Skip to main content
SpringerLink
Log in

Decreasing the Number of Evaluations in Evolutionary Algorithms by Using a Meta-model of the Fitness Function

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2610))

Included in the following conference series:

Abstract

In this paper a method is presented that decreases the necessary number of evaluations in Evolutionary Algorithms. A classifier with confidence information is evolved to replace time consuming evaluations during tournament selection. Experimental analysis of a mathematical example and the application of the method to the problem of evolving walking patterns for quadruped robots show the potential of the presented approach.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. W. Banzhaf, P. Nordin, R Keller, and F. Francone. Genetic Programming-An Introduction. dpunkt/Morgan Kaufmann, Heidelberg/San Francisco, 1998.

    MATH  Google Scholar 

  2. M. Emmerich, A. Giotis, M. Özdenir, T. Bäck, and K. Giannakoglou. Metamodel-assisted evolution strategies. In J. J. Merelo Guervos, P. Adamidis, H.-G. Beyer, J.-L. Fernandez-Villacanas, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature VII (PPSN), Lecture Notes in Computer Science, pages 361–370. Springer, Berlin, 2002.

    Chapter  Google Scholar 

  3. M. Giacobini, M. Tomassini, and L. Vanneschi. Limiting the number fitness cases in genetic programming using statistics. In J. J. Merelo et al., editor, Proccedings of the Seventh International Conference on Parallel Problem Solving from Nature (PPSN), Lecture Notes in Computer Science, LNCS 2439, pages 371–380. Springer Verlag, 2002.

    Google Scholar 

  4. G. S. Hornby, M. Fujita, S. Takamura, T. Yamamoto, and O. Hanagata. Autonomous evolution of gaits with the sony quadruped robot. In W. Banzhaf et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference, pages 1297–1304. Morgan Kaufmann, 1999.

    Google Scholar 

  5. AIM Learning Inc. Discipulus Owner’s Manual. http://www.aimlearning.com/Technology Overview.htm. WWW-Document.

  6. N. Jakobi, P. Husbands, and I. Harvey. Noise and the reality gap: The use of simulation in evolutionary robotics. In F. Moran et al., editor, Proceedings of the 3rd European Conference on Artificial Life, Lecture Notes in Artificial Intelligence, pages 704–720. Springer New York, Berlin, Heidelberg, 1995.

    Google Scholar 

  7. Y. Jin. Fitness Approximation in Evolutionary Computation-Bibliography. http://www.soft-computing.de/amec.html. WWW-Document.

  8. Y. Jin, M. Olhofer, and B. Sendhoff. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5):481–494, 2002.

    Article  Google Scholar 

  9. H.-S. Kim and S.-B. Cho. An efficient genetic algorithms with less fitness evaluation by clustering. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 887–894. IEEE Press, 2001.

    Google Scholar 

  10. M. Risler. InvKinWalkingEngine-Kurze Beschreibung der Parameter. Technische Universit ät Darmstadt, GermanTeam 2002, 2002.

    Google Scholar 

  11. K. Sastry, D. E. Goldberg, and M. Pelikan. Don’t evaluate, inherit. In L. Spector et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 551–558. Morgan Kaufmann, 2001.

    Google Scholar 

  12. H. Tagaki. Interactive evolutionary computation: System optimization based on human subjective evaluation. In IEEE International Conference on Intelligent Engineering Systems (INES), pages 1–6. IEEE Press, 1998.

    Google Scholar 

  13. J. Ziegler, J. Barnholt, J. Busch, and W. Banzhaf. Automatic evolution of control programs for a small humanoid walking robot. In P. Bidaud and F. Ben Amar, editors, Proceedings of the 5th International Conference on Climbing and Walking Robots (CLAWAR), pages 109–116. Professional Engineering Publishing, 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Dortmund, D-44221, Dortmund, Germany

    Jens Ziegler & Wolfgang Banzhaf

Authors
  1. Jens Ziegler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wolfgang Banzhaf
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Conor Ryan

  2. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    Terence Soule

  3. Department of Computer Science, Free University of Amsterdam, 1081, HV Amsterdam, The Netherlands

    Maarten Keijzer

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Edward Tsang  & Riccardo Poli  & 

  5. Department of Computer Science, University of Coimbra, Polo II, Pinhal Marrocos, 3030-290, Coimbra, Portugal

    Ernesto Costa

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ziegler, J., Banzhaf, W. (2003). Decreasing the Number of Evaluations in Evolutionary Algorithms by Using a Meta-model of the Fitness Function. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-36599-0_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00971-9

  • Online ISBN: 978-3-540-36599-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation