Discrete Dynamical Genetic Programming in XCS
arXiv:1204.4200 · doi:10.1145/1569901.1570075
Abstract
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.
arXiv admin note: substantial text overlap with arXiv:1201.5604