Generalization performance of vision based controllers for mobile robots evolved with genetic programming.

We present a genetic programming system to design automatically vision based obstacle avoidance algorithms adapted to the current context. We use a simulation environment to evaluate the controllers. By restricting the structure of the algorithms to facilitate the compromise between obstacle avoidance and target reaching, we improve the generalization performance of the algorithms.