Abstract
A series of simple biases to the selection of crossover points in treestructured genetic programming are investigated with respect to the provision of parsimonious solutions. Such a set of biases has a minimal computational overhead as they are based on information already used to estimate the fitness of individuals. Reductions to code bloat are demonstrated for the real world classification problems investigated. Moreover, bloated solutions provided by a uniform crossover operator often appear to defeat the application of MAPLETM simplification heuristics.
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References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992) ISBN 0-262-11170-5
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002) ISBN 3- 540-42451-2
Blickle, T., Thiele, L.: Genetic Programming and Redundancy, Genetic Algorithms within the Framework of Evolutionary Computation. In: Workshop at KI 1994, Max-Planck-Institut fur Informatik, MPI-1-94-241, pp. 33–38 (1994)
McPhee, N.F., Miller, J.D.: Accurate Replication in Genetic Programming. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 303–309. Morgan Kaufmann, San Francisco (1995)
Soule, T., Foster, J.A., Dickinson, J.: Code Growth in Genetic Programming. In: Proceedings of the 1st Annual Conference on Genetic Programming, pp. 215–223. MIT Press, Cambridge (1996)
Langdon, W.B., Poli, R.: Fitness Causes Bloat. In: 2nd On-line World Conference on Soft Computing in Engineering Design and Manufacturing, WSC2 (1997)
Soule, T., Foster, J.A.: Effects of Code Growth and Parsimony Pressure on Populations in Genetic Programming. Evolutionary Computation 6(4), 293–309 (1998)
Smith, P.W.H., Harries, K.: Code Growth, Explicitly Defined Introns, and Alternative Selection Schemes. Evolutionary Computation 6(4), 339–360 (1998)
Iba, H., de Garis, H.: Extending Genetic Programming with Recombinative Guidance. In: Angeline, P.J., Kinnear, K.E. (eds.) Advances in Genetic Programming II, Ch. 4, pp. 69–88. MIT Press, Cambridge (1996)
Borjarczuk, C.C., Lopes, H.S., Freitas, A.A.: Genetic Programming for Knowledge Discovery in Chest-Pain Diagnosis. IEEE Engineering in Medicine and Biology Magazine 19(4), 38–44 (2000)
Universal Problem Solvers Inc., Machine Learning Data Sets, http://pages.prodigy.com/upso/datasets.htm
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993) ISBN 1-55860- 238-0 (for c4.5 v c5.0 comparison see http://www.rulequest.com/ )
Waterloo Maple, Maple 8, http://www.mapleapps.com/
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© 2004 Springer-Verlag Berlin Heidelberg
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Terrio, M.D., Heywood, M.I. (2004). On Naïve Crossover Biases with Reproduction for Simple Solutions to Classification Problems. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_75
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DOI: https://doi.org/10.1007/978-3-540-24855-2_75
Publisher Name: Springer, Berlin, Heidelberg
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