Discrete Dynamical Genetic Programming in XCS
This is an incremental improvement for researchers in evolutionary computation and classifier systems, focusing on representation schemes.
The paper tackled the problem of representing rules in Learning Classifier Systems by using asynchronous random Boolean networks within XCS, showing that self-adaptive evolution can design ensembles to solve test problems.
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.