abstract = "The problem of overfitting (focusing closely on
examples at the loss of generalisation power) is
encountered in all supervised machine learning schemes.
This study is dedicated to explore some aspects of over
fitting in the particular case of genetic programming.
After recalling the causes usually invoked to explain
over-fitting such as hypothesis complexity or noisy
learning examples, we test and compare the resistance
to over fitting on three variants of genetic
programming algorithms (basic GP, sizefair crossover GP
and GP with boosting) on two benchmarks, a symbolic
regression and a classification problem. We propose
guidelines based on these results to help reduce over
fitting with genetic programming.",