This thesis deals with the problem of the Langton's artificial ant task and the use of tree-based and linear genetic programming as methods for solving this problem.

Trading System with Genetic Programming for Feature Engineering, Multilayer Perceptron Neural Network, Logistic Regression with Elastic Net Regularization and Support Vector Machines with L1 Regularization for Predictive Models and Genetic Algorithms for Hyperparameter Optimization.