Skip to main content

Meta-Evolution in Graph GP

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1598))

Abstract

In this contribution we investigate the evolution of operators for Genetic Programming by means of Genetic Programming. Metaevolution of recombination operators in graph-based GP is applied and compared to other methods for the variation of recombination operators in graph-based GP. We demonstrate that a straightforward application of recombination operators onto themselves does not work well. After introducing an additional level of recombination operators (the meta level) which are recombining a pool of recombination operators, even self-recombination on the additional level becomes feasible.We show that the overall performance of this system is better than in other variants of graph GP. As a test problem we use speaker recognition.

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

Buying options

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. P.J. Angeline. Multiple interacting programs: A representation for evolving complex behaviors. Cybernetics and Systems, 1998.

    Google Scholar 

  2. Th. Bäck. Self-adaptation. In Th. Bäck, D. B. Fogel, and Z. Michalewicz, editors, Handbook of Evolutionary Computation, page C7.1. IOP Publishing, Bristol and Oxford Univ. Press, New York, 1997.

    Chapter  Google Scholar 

  3. M. Brameier, P. Dittrich, W. Kantschik, and W. Banzhaf. SYSGP-A C++ library of different GP variants. Technical Report Internal Report of SFB 531,ISSN 1433-3325, Fachbereich Informatik, Universität Dortmund, 1998.

    Google Scholar 

  4. W. Banzhaf, P. Nordin, R. Keller, and F. Francone. Genetic Programming-An Introduction On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco and dpunkt.verlag, Heidelberg, 1998.

    MATH  Google Scholar 

  5. B. Edmonds. Meta-genetic programming: Co-evolving the operators of variation. CPM Report 98-32, Manchester Metropolitan University, 1998.

    Google Scholar 

  6. R.A. Finan, A.T. Sapeluk, and R.I. Damper. VQ score normalisation for text-dependent and text-independent speaker recognition. In Audio-and Video-based Biometric Person Authentication, pages 211–218. First International Conference, AVBPA’97, 1997.

    Google Scholar 

  7. J. Koza. Genetic Programming. MIT Press, 1992.

    Google Scholar 

  8. J. Koza. Genetic Programming II. MIT Press, 1994.

    Google Scholar 

  9. J. P. Nordin. A Compiling Genetic Programming System that Directly Manipulates the Machinecode. Cambridge, MIT Press, 1994.

    Google Scholar 

  10. R. Poli. Some steps towards a form of parallel distributed genetic programming. In The 1st Online Workshop on Soft Computing (WSC1), http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/, 19–30 August 1996. Nagoya University, Japan.

    Google Scholar 

  11. A.E. Rosenberg and F.K. Soong. Evaluation of a vector quantization talker recognition system in text independent and text dependent modes. Proc. ICASSP, pages 873–876, 1986.

    Google Scholar 

  12. A.E. Rosenberg and F.K. Soong. On the use of instantaneous and transitional spectral information in speaker recognition. Proc. ICASSP, pages 877–880, 1986.

    Google Scholar 

  13. H.-P. Schwefel. Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie (Inderdisciplinary Systems Research 26). Birkhäuser, Basel, 1977.

    Google Scholar 

  14. H.-P. Schwefel. Evolution and Optimum Seeking. John Wiley & Sons, Inc., 1996.

    Google Scholar 

  15. A. Teller. Evolving programmers: The co-evolution of intelligent recombination operators. In P. Angeline and K. Kinnear, editors, Advances in Genetic Programming II. MIT Press, 1996.

    Google Scholar 

  16. A. Teller and M. Veloso. Pado: A new learning architecture for object recognition. In Symbolic Visual Learning, pages 81–116. Oxford University Press, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kantschik, W., Dittrich, P., Brameier, M., Banzhaf, W. (1999). Meta-Evolution in Graph GP. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds) Genetic Programming. EuroGP 1999. Lecture Notes in Computer Science, vol 1598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48885-5_2

Download citation

  • DOI: https://doi.org/10.1007/3-540-48885-5_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65899-3

  • Online ISBN: 978-3-540-48885-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics