On dynamical genetic programming: simple Boolean networks in learning classifier systems
Larry Bull · International Journal of Parallel Emergent and Distributed Systems · 2009
Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic programming (GP). This paper presents results from an initial investigation into using simple dynamical GP representations 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.