  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 investigation into using a discrete
dynamical system representation within the XCS Learning Classifier System. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules. It is shown possible to
use self-adaptive, open-ended evolution to design an ensemble of such discrete
dynamical systems within XCS to solve a number of well-known test problems.
