Abstract
A few attempts to create taxonomies in evolutionary computation have been made. These either group algorithms or group problems on the basis of their similarities. Similarity is typically evaluated by manually analysing algorithms/problems to identify key characteristics that are then used as a basis to form the groups of a taxonomy. This task is not only very tedious but it is also rather subjective. As a consequence the resulting taxonomies lack universality and are sometimes even questionable. In this paper we present a new and powerful approach to the construction of taxonomies and we apply it to Genetic Programming (GP). Only one manually constructed taxonomy of problems has been proposed in GP before, while no GP algorithm taxonomy has ever been suggested. Our approach is entirely automated and objective. We apply it to the problem of grouping GP systems with their associated parameter settings. We do this on the basis of performance signatures which represent the behaviour of each system across a class of problems. These signatures are obtained thorough a process which involves the instantiation of models of GP’s performance. We test the method on a large class of Boolean induction problems.
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
Ashlock, D., Bryden, K., Corns, S.: On taxonomy of evolutionary computation problems. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Portland, Oregon, pp. 1713–1719. IEEE Press, Los Alamitos (2004)
Ashlock, D.A., Bryden, K.M., Corns, S., Schonfeld, J.: An updated taxonomy of evolutionary computation problems using graph-based evolutionary algorithms. In: Yen, G.G., Wang, L., Bonissone, P., Lucas, S.M. (eds.) Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 403–410. IEEE Press, Los Alamitos (2006)
Back, T., Fogel, D.B., Whitley, D., Angeline, P.J.: Mutation operators. In: Baeck, T., Fogel, D.B., Michalewicz, Z. (eds.) Evolutionary Computation 1 Basic Algorithms and Operators, ch. 32, pp. 237–255. Institute of Physics Publishing, Bristol (2000)
Calégari, P., Coray, G., Hertz, A., Kobler, D., Kuonen, P.: A taxonomy of evolutionary algorithms in combinatorial optimization. J. Heuristics 5(2), 145–158 (1999)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics (2004)
Graff, M., Poli, R.: Practical model of genetic programming’s performance on rational symbolic regression problems. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia-Alcázar, A., Falco, I.D., Cioppa, A.D., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 122–133. Springer, Heidelberg (2008)
Herrera, F., Lozano, M., Sánchez, A.M.: A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. Int. J. Intell. Syst. 18(3), 309–338 (2003)
Jansen, T., Wegener, I.: The analysis of evolutionary algorithms - a proof that crossover really can help. Algorithmica 34(1), 47–66 (2002)
Johnson, S.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
Martin, P.: Building a taxonomy of genetic programming. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), p. 182. Morgan Kaufmann, San Francisco (2001)
Nowostawski, M., Poli, R.: Parallel genetic algorithm taxonomy. In: Jain, L.C. (ed.) Proceedings of the Third International conference on knowledge-based intelligent information engineering systems (KES 1999), pp. 88–92. IEEE, Los Alamitos (1999)
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)
Poli, R., Logan, B.: The evolutionary computation cookbook: Recipes for designing new algorithms. In: Proceedings of the Second Online Workshop on Evolutionary Computation, Nagoya, Japan (March 1996)
Vose, M.D.: The simple genetic algorithm: Foundations and theory. MIT Press, Cambridge (1999)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
Zhang, B.-T.: A taxonomy of control schemes for genetic code growth. In: The Workshop on Evolutionary Computation with Variable Size Representation at ICGA 1997, July 20 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Graff, M., Poli, R. (2009). Automatic Creation of Taxonomies of Genetic Programming Systems. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds) Genetic Programming. EuroGP 2009. Lecture Notes in Computer Science, vol 5481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01181-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-01181-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01180-1
Online ISBN: 978-3-642-01181-8
eBook Packages: Computer ScienceComputer Science (R0)