An evolutionary algorithm for global induction of regression and model trees

Most tree-based algorithms are typical top-down approaches that search only for locally optimal decisions at each node and does not guarantee the globally optimal solution. In this paper, we would like to propose a new evolutionary algorithm for global induction of univariate regression trees and model trees that associate leaves with simple linear regression models. The general structure of our solution follows a typical framework of evolutionary algorithms with an unstructured population and a generational selection. We propose specialised genetic operators to mutate and cross-over individuals (trees), fitness function that base on the Bayesian information criterion and smoothing process that improves the prediction accuracy of the model tree. Performed experiments on 15 real-life datasets show that proposed solution can be significantly less complex with at least comparable performance to the classical top-down counterparts.