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Given the increasing interest in interpretable machine learning,classification trees have again attracted the attention of the scientificcommunity because of their glass-box structure. These models are usually builtusing greedy procedures, solving subproblems to find cuts in the feature spacethat minimize some impurity measures. In contrast to this standard greedyapproach and to the recent advances in the definition of the learning problemthrough MILP-based exact formulations, in this paper we propose a novelevolutionary algorithm for the induction of classification trees that exploitsa memetic approach that is able to handle datasets with thousands of points.Our procedure combines the exploration of the feasible space of solutions withlocal searches to obtain structures with generalization capabilities that arecompetitive with the state-of-the-art methods.

A Novel Memetic Strategy for Optimized Learning of Classification Trees