Discrete And Fuzzy Dynamical Genetic Programming In The Xcsf Learning Classifier System

# Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

**arXiv ID:** 1201.5604
**Authors:** Richard J. Preen, Larry Bull
**Published:** 2012-01-26T18:54:42Z
**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 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.

## Skill Description

This skill is generated from the arXiv paper: Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system (1201.5604).

## How to Use

[To be filled in by the user or by future automation]

## References

- [arXiv:1201.5604](http://arxiv.org/abs/1201.5604v2)