Abstract
Statistical techniques for designing and analyzing experiments are used to evaluate the individual and combined effects of genetic programming parameters. Three binary classification problems are investigated in a total of seven experiments consisting of 1108 runs of a machine code genetic programming system. The parameters having the largest effect in these experiments are the population size and the number of generations. A large number of parameters have negligible effects. The experiments indicate that the investigated genetic programming system is robust to parameter variations, with the exception of a few important parameters.
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References
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming -An Introduction. On the automatic evolution of computer programs and its applications. Morgan Kaufmann, Germany (1998)
Box, G.E., Hunter, W.G., Hunter, J.S.: Statistics for Experimenters - an Introduction to Design, Data Analysis and Model Building. Wiley & Sons, New York (1978)
Ehlich, H.: Determinantenabschatzungen fur binare Matrizen. Math. Z. 83, 123- 132 (1964)
Gathercole, C., Ross, P.: Dynamic Training Subset Selection for Supervised Learning in Genetic Programming, Chris. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866. Springer, Heidelberg (1994)
Kleijnen, J.P.C.: Experimental Design for Sensitivity Analysis, Optimization, and Validation of Simulation Models. In: Handbook of Simulation. Banks, Wiley & Sons, New York (1998)
Koza, J.R., Andre, D., Bennett, F.H., Keane, M.A.: Genetic Programming III: Darwinian Invention and Problem Solving. Academic Press/Morgan Kaufmann (1999)
Nordin, J.P.: Evolutionary Program Induction of Binary Machine Code and its Application. Krehl Verlag, Muenster (1997)
Nordin, J.P., Banzhaf, W., Francone, F.: Efficient Evolution of Machine Code for CISC Architectures using Blocks and Homologous Crossover. In: Langdon, O’Reilly, Angeline, Spector (eds.) To appear in Advances in Genetic Programming III. MIT-Press, USA (1999)
RML, Register Machine Learning Incorporated (1999), http://www.aimlearning.com
StatLib, Online Statistical resources library at the Department of Statistics, Carneige Mellon University, USA (1999), http://lib.stat.cmu.edu/
UCI ML repository. Files for the Pima-diabetes and Ionosphere problems from the Machine Learning repository at University of California, Irvine describing the Ionosphere problem (1999), http://www.ics.uci.edu/~mlearn
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© 2000 Springer-Verlag Berlin Heidelberg
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Feldt, R., Nordin, P. (2000). Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_20
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DOI: https://doi.org/10.1007/978-3-540-46239-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67339-2
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