MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

Abstract

In this article a set of well-known computational intelligence techniques such as Decision Trees, Fuzzy Logic, and Multi-objective Optimization Genetic Algorithm are combined to generate a novel hybrid method which is called MoGFT-I: Multi-objective Genetic Fuzzy Rule Based System supported by a Decision Tree with Improved Interpretability. The output of the proposed supervised learning method is a set of Mamdani-type fuzzy systems which are optimized and distributed along a Pareto curve by considering two conflicting attributes: accuracy and interpretability. The MoGFT-I method is then applied to the Cart and Pole control problem such that a set of feedback controller are designed to control this unstable nonlinear dynamical system.