Evolution of robot controller using cartesian genetic programming

Abstract. Cartesian Genetic Programming is a graph based representa-tion that has many benefits over traditional tree based methods, includ-ing bloat free evolution and faster evolution through neutral search. Here, an integer based version of the representation is applied to a traditional problem in the field: evolving an obstacle avoiding robot controller. The technique is used to rapidly evolve controllers that work in a complex en-vironment and with a challenging robot design. The generalisation of the robot controllers in different environments is also demonstrated. A novel fitness function based on chemical gradients is presented as a means of improving evolvability in such tasks.