abstract = "There have been many studies undertaken to determine
the efficacy of parameters and algorithmic components
of Genetic Programming, but historically,
generalisation considerations have not been of central
importance in such investigations. Recent contributions
have stressed the importance of generalization to the
future development of the field. In this paper we
investigate aspects of selection bias as a component of
generalisation error, where selection bias refers to
the method used by the learning system to select one
hypothesis over another. Sources of potential bias
include the replacement strategy chosen and the means
of applying selection pressure. We investigate the
effects on generalisation of two replacement
strategies, together with tournament selection with a
range of tournament sizes. Our results suggest that
larger tournaments are more prone to overfitting than
smaller ones, and that a small tournament combined with
a generational replacement strategy produces relatively
small solutions and is least likely to over-fit.",