  Most decision tree induction algorithms are based on a greedy top-down
recursive partitioning strategy for tree growth. In this paper, we propose
several methods for induction of decision trees and their ensembles based on
evolutionary algorithms. The main difference of our approach is using
real-valued vector representation of decision tree that allows to use a large
number of different optimization algorithms, as well as optimize the whole tree
or ensemble for avoiding local optima. Differential evolution and evolution
strategies were chosen as optimization algorithms, as they have good results in
reinforcement learning problems. We test the predictive performance of this
methods using several public UCI data sets, and the proposed methods show
better quality than classical methods.
