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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
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)
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)
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)
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)
O’Neill, M., Ryan, C.: Grammatical Evolution. Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003)
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)
O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Transactions on Evolutionary Computation 5(4), 349–358 (2001)
Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Berlin (2006)
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)
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)
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)
Dempsey, I., O’Neill, M., Brabazon, A.: Constant creation in grammatical evolution. International Journal of Innovative Computing and Applications 1(1), 23–38 (2007)
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)
Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Montes de Oca, M.A. (2008). Exposing a Bias Toward Short-Length Numbers in Grammatical Evolution. In: O’Neill, M., et al. Genetic Programming. EuroGP 2008. Lecture Notes in Computer Science, vol 4971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78671-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-540-78671-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78670-2
Online ISBN: 978-3-540-78671-9
eBook Packages: Computer ScienceComputer Science (R0)