A brief note on discrete dynamical learning classifier systems
Larry Bull · UWE Research Repository (UWE Bristol) · 2008
A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an initial investigation into using a discrete dynamical system representation within an accuracy-based Learning Classifier System. In particular, random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to evolve an ensemble of such discrete dynamical systems to solve versions of the well-known Boolean multiplexer problem.