Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
This work addresses representation challenges in learning classifier systems for AI researchers, but it is incremental as it builds on existing XCSF frameworks.
The paper tackled the problem of representing rules in learning classifier systems by using discrete and fuzzy dynamical systems within XCSF, 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 discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.