The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithm capable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm.
Cite
@article{arxiv.1902.03983,
title = {Interaction-Transformation Evolutionary Algorithm for Symbolic Regression},
author = {Fabricio Olivetti de Franca and Guilherme Seidyo Imai Aldeia},
journal= {arXiv preprint arXiv:1902.03983},
year = {2020}
}
Comments
25 pages, 9 tables, 3 figures, submitted to Evolutionary Computation Journal, September 2020