An Evolutionary Algorithm for Global Induction of Regression Trees with Multivariate Linear Models

In the paper we present a new evolutionary algorithm for induction of regression trees. In contrast to the typical top-down approaches it globally searches for the best tree structure, tests at internal nodes and models at the leaves. The general structure of proposed solution follows a framework of evolutionary algorithms with an unstructured population and a generational selection. Specialized genetic operators efficiently evolve regression trees with multivariate linear models. Bayesian information criterion as a fitness function mitigate the over-fitting problem. The preliminary experimental validation is promising as the resulting trees are less complex with at least comparable performance to the classical top-down counterpart.