Amplitude-oriented mixed-type CGP classification

Karlo Knezevic, Stjepan Picek, Julian F. Miller

Abstract: Evolutionary algorithms have proved their worth on various optimization problems over the course of years. However, some techniques like genetic programming (GP) and Cartesian genetic programming (CGP) are not restricted only to optimization problems but can be also used in classification tasks. In this paper, we consider mixed-type CGP (MT-CGP) and test it on a number of benchmark binary and multi-class problems. Following that, we introduce a new representation for our algorithm where each node also has an accompanying weight factor called the amplitude. Our results suggest that this version of CGP is more powerful and able to obtain higher accuracies when compared to the mixed-type CGP or the standard CGP. Finally, we introduce the LI regularization into MT-CGP in order to facilitate even further feature reduction.