Evolving balanced decision trees with a multi-population genetic algorithm

Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multipopulation genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely tend to construct decision trees with great variance in single class accuracies. The proposed approach is tested over 10 UCI datasets, and it is compared with a single-population genetic algorithm as well as with traditional decision-tree induction algorithms. Results show that the designed multi-population approach provides classification results comparable to C4.5 and CART in terms of accuracy and tree size, while outperforming them regarding balanced solutions (in terms of average class accuracy and range of single-class accuracies).