abstract = "This chapter proposes a new model of tree-based
Genetic Programming (GP) as a simple sampling algorithm
that samples minimal schemata (subsets of the solution
space) described by a single concrete node at a single
position in the expression tree. We show that GP
explores these schemata in the same way across three
benchmarks, rapidly converging the population to a
specific function at each position throughout the upper
layers of the expression tree. This convergence is
driven by covariance between membership of a simple
schema and rank fitness. We model this process using
Prices theorem \cite{price:nature} and provide
empirical evidence to support our model. The chapter
closes with an outline of a modification of the
standard GP algorithm that reinforces this bias by
converging populations to fit schemata in an
accelerated way.",