  Fitness functions based on test cases are very common in Genetic Programming
(GP). This process can be assimilated to a learning task, with the inference of
models from a limited number of samples. This paper is an investigation on two
methods to improve generalization in GP-based learning: 1) the selection of the
best-of-run individuals using a three data sets methodology, and 2) the
application of parsimony pressure in order to reduce the complexity of the
solutions. Results using GP in a binary classification setup show that while
the accuracy on the test sets is preserved, with less variances compared to
baseline results, the mean tree size obtained with the tested methods is
significantly reduced.
