Application of genetic programming algorithm for designing decision trees and their ensembles

Decision tree is a machine learning algorithm that is very effective in classification problems. Decision tree is a topical algorithm because of the good interpretability of the results of their work. However, decision tree has the drawback that standard algorithms don't allow obtaining the optimal structure of the decision tree. To solve this drawback, it's proposed to use the genetic programming algorithm. This algorithm is one of the branches of evolutionary algorithms and has proven itself for the design of intelligent information technologies. Genetic programming, in one of their implementations, searches for solutions in tree space, which is well suited for designing decision trees. Ensembles that are based on decision trees have high efficiency. In this paper, random forest and gradient boosting are considered. In ensembles, it's proposed to combine decision trees that are designed by the genetic programming algorithm. Algorithms was tested on classification problems.