Interaction-Transformation Evolutionary Algorithm for Symbolic Regression

Autor:	de Franca, Fabricio Olivetti, Aldeia, Guilherme Seidyo Imai
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 25 pages, 9 tables, 3 figures, submitted to Evolutionary Computation Journal, September 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1902.03983 View this record from Arxiv