Some Training Subset Selection Methods for Supervised Learning in Genetic Programming

Abstract

When using the Genetic Programming (GP) Algorithm on a diicult problem with a large set of training cases, a large population size is needed and a very large number of function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on which to actually… (More)

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