  We introduce in this paper a runtime-efficient tree hashing algorithm for the
identification of isomorphic subtrees, with two important applications in
genetic programming for symbolic regression: fast, online calculation of
population diversity and algebraic simplification of symbolic expression trees.
Based on this hashing approach, we propose a simple diversity-preservation
mechanism with promising results on a collection of symbolic regression
benchmark problems.
