On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems

Many representations have been presented to enable the effective evolution of computer programs. Turning was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turning proposed a form of, discrete dynamical system and yet dynamical representations remain almost unexplored within genetic programming. This paper presents results from an initial investigation into using a simple dynamical genetic programming representation within a Learning Classifier System. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.