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On Crossover Success Rate in Genetic Programming with Offspring Selection

  • Gabriel Kronberger
  • Stephan Winkler
  • Michael Affenzeller
  • Stefan Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are still largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring (success rate) when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators does not improve the performance of the algorithm in terms of best solution quality or efficiency.

Keywords

Mean Square Error Selection Pressure Genetic Programming Solution Quality Tree Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Affenzeller, M., Wagner, S.: Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In: Adaptive and Natural Computing Algorithms. Springer Computer Series, pp. 218–221. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Affenzeller, M., Winkler, S.M., Wagner, S.: Effective allele preservation by offspring selection: An empirical study for the TSP. International Journal of Simulation and Process Modelling (2009) (accepted to appear)Google Scholar
  3. 3.
    Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)Google Scholar
  4. 4.
    Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  5. 5.
    Langdon, W.B.: Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines 1(1/2), 95–119 (2000)CrossRefzbMATHGoogle Scholar
  6. 6.
    Langdon, W.B., Banzhaf, W.: Repeated patterns in genetic programming. Natural Computing (2008) (Published online: May 26, 2007)Google Scholar
  7. 7.
    Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Trans. Evolutionary Computation 4(3), 274–283 (2000)CrossRefGoogle Scholar
  9. 9.
    Murphy, G., Ryan, C.: Exploiting the path of least resistance in evolution. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and evolutionary computation, Atlanta, GA, USA, pp. 1251–1258. ACM, New York (2008)Google Scholar
  10. 10.
    Murphy, G., Ryan, C.: A simple powerful constraint for genetic programming. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 146–157. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Page, J., Poli, R., Langdon, W.B.: Smooth uniform crossover with smooth point mutation in genetic programming: A preliminary study. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 39–49. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Poli, R.: A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 204–217. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Poli, R., Langdon, W.B.: On the search properties of different crossover operators in genetic programming. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, pp. 293–301. Morgan Kaufmann, San Francisco (1998)Google Scholar
  14. 14.
    Poli, R., McPhee, N.F.: General schema theory for genetic programming with subtree-swapping crossover: part I. Evol. Comput. 11(1), 53–66 (2003)CrossRefGoogle Scholar
  15. 15.
    Poli, R., McPhee, N.F.: General schema theory for genetic programming with subtree-swapping crossover: part II. Evol. Comput. 11(2), 169–206 (2003)CrossRefGoogle Scholar
  16. 16.
    Poli, R., McPhee, N.F., Rowe, J.E.: Exact schema theory and markov chain models for genetic programming and variable-length genetic algorithms with homologous crossover. Genetic Programming and Evolvable Machines 5(1), 31–70 (2004)CrossRefGoogle Scholar
  17. 17.
    Poli, R., Rowe, J.E., Stephens, C.R., Wright, A.H.: Allele diffusion in linear genetic programming and variable-length genetic algorithms with subtree crossover. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 212–227. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Winkler, S.M., Affenzeller, M., Wagner, S.: Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis. In: Genetic Programming and Evolvable Machines (2009) (Online First)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gabriel Kronberger
    • 1
  • Stephan Winkler
    • 1
  • Michael Affenzeller
    • 1
  • Stefan Wagner
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory School of InformaticsCommunications and Media - Hagenberg, Upper Austria University of Applied SciencesHagenbergAustria

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