Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
arXiv:2107.10640 · doi:10.1007/2F978-3-030-45093-9_44
Abstract
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.
International Conference on Computer Aided Systems Theory, EUROCAST 2019