Advertisement

Exposing a Bias Toward Short-Length Numbers in Grammatical Evolution

  • Marco A. Montes de Oca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4971)

Abstract

Many automatically-synthesized programs have, like their hand-made counterparts, numerical parameters that need to be set properly before they can show an acceptable performance. Hence, any approach to the automatic synthesis of programs needs the ability to tune numerical parameters efficiently.

Grammatical Evolution (GE) is a promising grammar-based genetic programming technique that synthesizes numbers by concatenating digits. In this paper, we show that a naive application of this approach can lead to a serious number length bias that in turn affects efficiency. The root of the problem is the way the context-free grammar used by GE is defined. A simple, yet effective, solution to this problem is proposed.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  2. 2.
    Koza, J.R.: Automatic synthesis of topologies and numerical parameters. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 83–104. Kluwer Academic Publishers, Boston (2003)CrossRefGoogle Scholar
  3. 3.
    Evett, M., Fernandez, T.: Numeric mutation improves the discovery of numeric constants in genetic programming. In: Koza, J.R., et al. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 66–71. Morgan Kaufmann, San Francisco (1998)Google Scholar
  4. 4.
    Topchy, A., Punch, W.F.: Faster genetic programming based on local gradient search of numeric leaf values. In: Spector, L., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 155–162. Morgan Kaufmann, San Francisco, CA, USA (2001)Google Scholar
  5. 5.
    Li, X., Zhou, C., Nelson, P.C., Tirpak, T.M.: Investigation of constant creation techniques in the context of gene expression programming. In: In Keijzer, M. (ed.) GECCO 2004. LNCS, vol. 3103, Springer, Heidelberg (2004) (Late Breaking Paper)Google Scholar
  6. 6.
    O’Neill, M., Ryan, C.: Grammatical Evolution. Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003)zbMATHGoogle Scholar
  7. 7.
    O’Neill, M., Dempsey, I., Brabazon, A., Ryan, C.: Analysis of a digit concatenation approach to constant creation. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 173–182. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Transactions on Evolutionary Computation 5(4), 349–358 (2001)CrossRefGoogle Scholar
  9. 9.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Berlin (2006)zbMATHGoogle Scholar
  10. 10.
    Cleary, R., O’Neill, M.: An Attribute Grammar Decoder for the 01 MultiConstrained Knapsack Problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 34–45. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Tsoulos, I.G., Gavrilis, D., Glavas, E.: Neural network construction using grammatical evolution. In: Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, Piscataway, NJ, USA, pp. 827–831. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  12. 12.
    Dempsey, I., O’Neill, M., Brabazon, A.: Grammatical constant creation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 447–458. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Dempsey, I., O’Neill, M., Brabazon, A.: Constant creation in grammatical evolution. International Journal of Innovative Computing and Applications 1(1), 23–38 (2007)CrossRefGoogle Scholar
  14. 14.
    Dempsey, I., O’Neill, M., Brabazon, A.: meta-Grammar Constant Creation with Grammatical Evolution by Grammatical Evolution. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 1665–1671. ACM Press, New York (2005)CrossRefGoogle Scholar
  15. 15.
    Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marco A. Montes de Oca
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations