Skip to main content

Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming

  • Conference paper
Genetic Programming (EuroGP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4445))

Included in the following conference series:

Abstract

Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) capable of acquiring, evolving and re-using partial solutions. In this paper, we apply for the first time CGP and ECGP to the ones-max and order-3 deceptive problems, which are normally associated with Genetic Algorithms. Our approach uses CGP and ECGP to evolve a sequence of commands for a tape-head, which produces an arbitrary length binary string on a piece of tape. Computational effort figures are calculated for CGP and ECGP and our results compare favourably with those of Genetic Algorithms.

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

Access this chapter

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. Angeline, P.J., Pollack, J.: Evolutionary module acquisition. In: Proc. of the 2nd Annual Conference on Evolutionary Programming, pp. 154–163 (1993)

    Google Scholar 

  2. Walker, J.A., Miller, J.F.: Investigating the performance of module acquisition in cartesian genetic programming. In: Proc. of GECCO, vol. 2, pp. 1649–1656. ACM, New York (2005)

    Chapter  Google Scholar 

  3. Walker, J.A., Miller, J.F.: Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems. In: Proc. of GECCO, ACM, New York (2006)

    Google Scholar 

  4. Ackley, D.H.: A connectionist Machine for Genetic Hillclimbing. Kluwer, Dordrecht (1987)

    Google Scholar 

  5. Tuson, A., Ross, P.: Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2) (1998)

    Google Scholar 

  6. Goldberg, D.E., Deb, K., Korb, B.: Messy genetic algortihms: Motivation, analysis and first results. Complex Systems 3(5) (1989)

    Google Scholar 

  7. Yu, T., Miller, J.F.: The role of neutral and adaptive mutation in an evolutionary search on the onemax problem. In: Late Breaking Papers at GECCO, pp. 512–519. AAAI, Menlo Park (2002)

    Google Scholar 

  8. Ryan, C., Nicolau, M., O’Neill, M.: Genetic algorithms uing grammatical evolution. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 278–287. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  9. Nicolau, M., Ryan, C.: Linkgauge: Tackiling hard deceptive problems with a new linkage learning genetic algortihm. In: Proc. of GECCO, pp. 488–494. AAAI, Menlo Park (2002)

    Google Scholar 

  10. O’Neill, M., Brabazon, A.: mGGA:The meta-grammar genetic algorithm. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J.I., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 311–320. Springer, Heidelberg (2005)

    Google Scholar 

  11. Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)

    Google Scholar 

  12. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Marc Ebner Michael O’Neill Anikó Ekárt Leonardo Vanneschi Anna Isabel Esparcia-Alcázar

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Walker, J.A., Miller, J.F. (2007). Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71605-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71602-0

  • Online ISBN: 978-3-540-71605-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics