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