Skip to main content
SpringerLink
Log in

A Puzzle to Challenge Genetic Programming

  • Conference paper
  • First Online:
Book cover Genetic Programming (EuroGP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2278))

Included in the following conference series:

Abstract

This report represents an initial investigation into the use of genetic programming to solve the N-prisoners puzzle. The puzzle has generated a certain level of interest among the mathematical community. We believe that this puzzle presents a significant challenge to the field of evolutionary computation and to genetic programming in particular. The overall aim is to generate a solution that encodes complex decision making. Our initial results demonstrate that genetic programming can evolve good solutions. We compare these results to engineered solutions and discuss some of the implications. One of the consequences of this study is that it has highlighted a number of research issues and directions and challenges for the evolutionary computation community. We conclude the article by presenting some of these directions which range over several areas of evolutionary computation, including multi-objective fitness, coevolution and cooperation, and problem representations.

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. W.F. Punch, D. Zongker, and E.D. Goodman. The roya tree problem, a benchmark for single and multi-population genetic programming. In P.J. Angeline and K.E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 15, pages 299–316. The MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  2. W.B. Langdon. Data Structures and Genetic Programming:Genetic Programming + Data Structures =Automatic Programming!, volume 1 of Genetic Programming. Kluwer, Boston, 24 April 1998.

    Google Scholar 

  3. J.R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.

    MATH  Google Scholar 

  4. J. Koza, F. Bennett, and D. Andre. Using programmatic motifs and genetic programming to classify protein sequences as to extracellular and membrane cellular location. In V. William Porto et al, editor, Proceedings of the Seventh Annual Conference on Evolutionary Programming, volume 1447 of LNCS, San Diego, California, USA, 25–27 March 1998. Springer-Verlag.

    Google Scholar 

  5. J.M. Daida, J.A. Polito, S.A. Stanhope, R.R. Bertram, J.C. Khoo, and S.A. Chaudhary. What makes a problem GP-hard? analysis of a tunably difficult problem in genetic programming. In Wolfgang Banzhaf et al, editor, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 982–989, Orlando, Florida, USA, 13–17 July 1999. Morgan Kaufmann.

    Google Scholar 

  6. S. Luke. Genetic programming produced competitive soccer softbot teams for robocup97. In J.R. Koza et al, editor, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 214–222, University of Wisconsin, Madison, Wisconsin, USA, 22–25 July 1998. Morgan Kaufmann.

    Google Scholar 

  7. D. Andre and A. Teller. Evolving Team Darwin United. In M. Asada and H. Kitano, editors, RoboCup-98: Robot Soccer World Cup II, volume 1604 of LNCS, pages 346–351, Paris, France, July 1998 1999. Springer Verlag.

    Chapter  Google Scholar 

  8. S.M. Gustafson and W.H. Hsu. Layered learning in genetic programming for a co-operative robot soccer problem. In J.F. Miller et al, editor, Proceedings of the European Conference on Genetic Programming, volume 2038 of LNCS, Lake Como, Italy, April 2001. Springer-Verlag.

    Google Scholar 

  9. T. Soule, J.A. Foster, and J. Dickinson. Using genetic programming to approximate maximum clique. In J.R. Koza et al, editor, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 400–405, Stanford University, CA, USA, 28–31 July 1996. MIT Press.

    Google Scholar 

  10. T. Ebert. Applications of Recursive Operators to Randomness and Complexity. Ph.D. thesis, University of California at Santa Barbara, 1998.

    Google Scholar 

  11. T. Ebert. On the autoreducibility of random sequences. Unpublished. http://www.ics.uci.edu/.ebert/, 2001.

  12. S. Robinson. Why mathematicians now care about their hat color. The New York Times:Science Desk, 10 April 2001.

    Google Scholar 

  13. R.W. Hamming. Coding and Information Theory. Prentice-Hall, Inc, New Jersey, USA, 1980.

    Google Scholar 

  14. S. Luke. Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat. PhD thesis, Department of Computer Science, University of Maryland, University of Maryland, College Park, MD 20742 USA, 2000.

    Google Scholar 

  15. J.D. Knowles, R.A. Watson, and D.W. Corne. Reducing Local Optima in Single-Objective Problems by Multi-objectivization. In E. Zitzler et al, editor, First International Conference on Evolutionary Multi-Criterion Optimization, pages 268–282. Springer-Verlag. LNCS no. 1993, 2001.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ASAP Research, School of Computer Science & IT, University of Nottingham, UK

    Edmund Burke, Steven Gustafson & Graham Kendall

Authors
  1. Edmund Burke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven Gustafson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graham Kendall
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    James A. Foster

  2. INRIA Rocquencourt, B.P. 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton

  3. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, UK

    Julian Miller

  4. Department of Computer Science and Information Systems, University of Limerick, Schuman Building, Limerick, Ireland

    Conor Ryan

  5. Dip. Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Andrea Tettamanzi

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Burke, E., Gustafson, S., Kendall, G. (2002). A Puzzle to Challenge Genetic Programming. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds) Genetic Programming. EuroGP 2002. Lecture Notes in Computer Science, vol 2278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45984-7_23

Download citation

  • DOI: https://doi.org/10.1007/3-540-45984-7_23

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43378-1

  • Online ISBN: 978-3-540-45984-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation