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.
Cite
CITATION STYLE
Feldt, R., & Nordin, P. (2000). Using factorial experiments to evaluate the effect of genetic programming parameters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1802, pp. 271–282). Springer Verlag. https://doi.org/10.1007/978-3-540-46239-2_20
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