Advertisement

Cartesian Genetic Programming: Why No Bloat?

  • Andrew James Turner
  • Julian Francis Miller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)

Abstract

For many years now it has been known that Cartesian Genetic Programming (CGP) does not exhibit program bloat. Two possible explanations have been proposed in the literature: neutral genetic drift and length bias. This paper empirically disproves both of these and thus, reopens the question as to why CGP does not suffer from bloat. It has also been shown for CGP that using a very large number of nodes considerably increases the effectiveness of the search. This paper also proposes a new explanation as to why this may be the case.

Keywords

Genetic Programming Active Node Symbolic Regression Program Size Parity Task 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evolutionary Computation 14(3), 309–344 (2006)CrossRefGoogle Scholar
  2. 2.
    Silva, S., Costa, E.: Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines 10(2), 141–179 (2009)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Miller, J.: What bloat? Cartesian genetic programming on Boolean problems. In: 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, pp. 295–302 (2001)Google Scholar
  4. 4.
    Goldman, B.W., Punch, W.F.: Length bias and search limitations in Cartesian genetic programming. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, pp. 933–940. ACM (2013)Google Scholar
  5. 5.
    Miller, J., Smith, S.: Redundancy and computational efficiency in Cartesian genetic programming. IEEE Transactions on Evolutionary Computation 10(2), 167–174 (2006)CrossRefGoogle Scholar
  6. 6.
    Rothlauf, F., Goldberg, D.E.: Representations for Genetic and Evolutionary Algorithms. Physica-Verlag (2002)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Miller, D.J.F. (ed.): Cartesian Genetic Programming. Springer (2011)Google Scholar
  9. 9.
    Vassilev, V.K., Miller, J.F.: The Advantages of Landscape Neutrality in Digital Circuit Evolution. In: Miller, J.F., Thompson, A., Thompson, P., Fogarty, T.C. (eds.) ICES 2000. LNCS, vol. 1801, pp. 252–263. Springer, Heidelberg (2000)Google Scholar
  10. 10.
    Yu, T., Miller, J.F.: Neutrality and the evolvability of boolean function landscape. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 204–217. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Poli, R., Langdon, W.W.B., McPhee, N.F., Koza, J.R.: A field guide to genetic programming (2008), Published via, http://lulu.com and freely available at http://www.gp-field-guide.org.uk
  12. 12.
    Vanneschi, L., Castelli, M., Silva, S.: Measuring bloat, overfitting and functional complexity in genetic programming. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 877–884. ACM (2010)Google Scholar
  13. 13.
    Soule, T., Heckendorn, R.B.: An analysis of the causes of code growth in genetic programming. Genetic Programming and Evolvable Machines 3(3), 283–309 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Langdon, W., Soule, T., Poli, R., Foster, J.: The evolution of size and shape. Advances in Genetic Programming 3, 163 (1999)Google Scholar
  15. 15.
    Kimura, M.: The neutral theory of molecular evolution. Cambridge University Press (1984)Google Scholar
  16. 16.
    McDermott, J., White, D.R., Luke, S., Manzoni, L., Castelli, M., Vanneschi, L., Jaskowski, W., Krawiec, K., Harper, R., De Jong, K., et al.: Genetic programming needs better benchmarks. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, pp. 791–798. ACM (2012)Google Scholar
  17. 17.
    Goldman, B.W., Punch, W.F.: Reducing wasted evaluations in cartesian genetic programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 61–72. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew James Turner
    • 1
  • Julian Francis Miller
    • 1
  1. 1.Electronics DepartmentUniversity of YorkHeslingtonUK

Personalised recommendations