Advertisement

Genetic Programming Crossover: Does It Cross over?

  • Colin G. Johnson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

One justification for the use of crossover operators in Genetic Programming is that the crossover of program syntax gives rise to the crossover of information at the semantic level. In particular, a fitness-increasing crossover is presumed to act by combining fitness-contributing components of both parents. In this paper we investigate a particular interpretation of this hypothesis via an experimental study of 70 GP runs, in which we categorise each crossover event by its fitness properties and the information that contributes most strongly to those fitness properties. Some tentative evidence in support of the above hypothesis is extracted from this categorisation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Beadle, L., Johnson, C.G.: Sematically driven crossover in genetic programming. In: Proceedings of the 2008 IEEE World Congress on Computational Intelligence. IEEE Press, Los Alamitos (2008)Google Scholar
  3. 3.
    Keijzer, M.: Improving symbolic regression with interval arithmetic. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 70–82. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Keijzer, M., Babovic, V.: Genetic programming, ensemble methods and the bias/variance tradeoff - introductory investigations. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 76–90. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by means of Natural Selection. Series in Complex Adaptive Systems. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  6. 6.
    Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  7. 7.
    Nordin, P., Banzhaf, W.: Complexity compression and evolution. In: Eshelman, L. (ed.) Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA 1995), Pittsburgh, PA, USA, pp. 310–317. Morgan Kaufmann, San Francisco (1995)Google Scholar
  8. 8.
    Nordin, P., Francone, F., Banzhaf, W.: Explicitly defined introns and destructive crossover in genetic programming. In: Rosca, J.P. (ed.) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, California, USA, July 9, pp. 6–22 (1995)Google Scholar
  9. 9.
    O’Reilly, U.-M., Oppacher, F.: Hybridized crossover-based search techniques for program discovery. In: Proceedings of the 1995 World Conference on Evolutionary Computation, Perth, Australia, November 29- December 11995, vol. 2, pp. 573–578. IEEE Press, Los Alamitos (1995)CrossRefGoogle Scholar
  10. 10.
    Poli, R.: TinyGP software (visited November 2008), http://cswww.essex.ac.uk/staff/rpoli/TinyGP/
  11. 11.
    Poli, R., Langdon, W.B.: On the search properties of different crossover operators in genetic programming. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, July 22-25, 1998, pp. 293–301. Morgan Kaufmann, San Francisco (1998)Google Scholar
  12. 12.
    Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008), http://lulu.com http://www.gp-field-guide.org.uk (With contributions by Koza, J.R.)
  13. 13.
    Ryan, C.: Pygmies and civil servants. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 243–263. MIT Press, Cambridge (1994)Google Scholar
  14. 14.
    Salustowicz, R., Schmidhuber, J.: Probabilistic incremental program evolution. Evolutionary Computation 5(2), 123–141 (1997)CrossRefGoogle Scholar
  15. 15.
    Sanchez, L.: Interval-valued GA-P algorithms. IEEE Transactions on Evolutionary Computation 4(1), 64–72 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Colin G. Johnson
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterburyEngland

Personalised recommendations