All Outputs (3)

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.

An XCS approach to forecasting financial time series (2009)
Presentation / Conference
Preen, R. (2009, June). An XCS approach to forecasting financial time series. Paper presented at GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, Montreal, Canada

This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed in the envir... Read More about An XCS approach to forecasting financial time series.