Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system (2013)
Journal Article
Preen, R., & Bull, L. (2014). Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18(1), 153-167. https://doi.org/10.1007/s00500-013-1044-4

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 repr... Read More about Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system.

Dynamical genetic programming in XCSF (2013)
Journal Article
Preen, R. J., Preen, R., & Bull, L. (2013). Dynamical genetic programming in XCSF. Evolutionary Computation, 21(3), 361-387. https://doi.org/10.1162/EVCO_a_00080

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic... Read More about Dynamical genetic programming in XCSF.

Arithmetic dynamical genetic programming in the XCSF learning classifier system (2011)
Presentation / Conference
Preen, R., & Bull, L. (2011, June). Arithmetic dynamical genetic programming in the XCSF learning classifier system. Paper presented at IEEE Congress on Evolutionary Computation (CEC), 2011, New Orleans, US

This paper presents results from an investigation into using a continuous-valued dynamical system representation within the XCSF Learning Classifier System. In particular, dynamical arithmetic genetic networks are used to represent the traditional... Read More about Arithmetic dynamical genetic programming in the XCSF learning classifier system.

On dynamical genetic programming: Random boolean networks in learning classifier systems (2009)
Journal Article
Bull, L., & Preen, R. (2009). On dynamical genetic programming: Random boolean networks in learning classifier systems. Lecture Notes in Artificial Intelligence, 5481 LNCS, 37-48. https://doi.org/10.1007/978-3-642-01181-8_4

Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system... Read More about On dynamical genetic programming: Random boolean networks in learning classifier systems.

Discrete dynamical genetic programming in XCS (2009)
Presentation / Conference
Preen, R., & Bull, L. (2009, July). Discrete dynamical genetic programming in XCS. Paper presented at 11th Annual conference on Genetic and evolutionary computation, Montreal, Canada

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 representati... Read More about Discrete dynamical genetic programming in XCS.