Skip to main content

Research Repository

Advanced Search

All Outputs (275)

Non-linear media based computers: chemical and neuronal networks through machine learning (2008)
Report
Bull, L., Adamatzky, A., de Lacy Costello, B., Husbands, P., O’Shea, M., Purcell, W., & Taylor, A. (2008). Non-linear media based computers: chemical and neuronal networks through machine learning

There is growing interest in research into the development of hybrid wetware-silicon devices focused on exploiting their potential for 'non-linear computing'. The aim is to harness the as yet only partially understood intricate dynamics of non-linear... Read More about Non-linear media based computers: chemical and neuronal networks through machine learning.

Knowledge discovery from medical data: an empirical study with XCS (2008)
Book Chapter
Kharbat, F., Odeh, M., & Bull, L. (2008). Knowledge discovery from medical data: an empirical study with XCS. In L. Bull, E. Bernado-Mansilla, & J. Holmes (Eds.), Learning Classifier Systems in Data Mining. Springer

In this chapter we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a primary breast cancer data set. Our results indicate more effective knowled... Read More about Knowledge discovery from medical data: an empirical study with XCS.

Towards clustering with learning classifier systems (2008)
Book Chapter
Tamee, K., Bull, L., & Pinngern, O. (2008). Towards clustering with learning classifier systems. In L. Bull, E. Bernadó-Mansilla, & J. Holmes (Eds.), Learning classifier systems in data mining (191-204). Springer

Using the XCS classifier system for multi-objective reinforcement learning problems (2007)
Journal Article
Studley, M., & Bull, L. (2007). Using the XCS classifier system for multi-objective reinforcement learning problems. Artificial Life, 13(1), 69-86. https://doi.org/10.1162/artl.2007.13.1.69

We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy c... Read More about Using the XCS classifier system for multi-objective reinforcement learning problems.

Anticipation mappings for learning classifier systems (2007)
Presentation / Conference
Bull, L., O'Hara, T., & Lanzi, P. L. (2007, September). Anticipation mappings for learning classifier systems. Paper presented at Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, Singapore

Accuracy-based learning classifier system ensembles with rule-sharing (2007)
Journal Article
Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Accuracy-based learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163

Learning Classifier Systems (LCS) are a method of evolving compact rule-sets using reinforcement learning. This paper presents an investigation into exploiting the population-based nature of LCS for their use within highly-parallel systems, such as... Read More about Accuracy-based learning classifier system ensembles with rule-sharing.

Toward a better understanding of rule initialisation and deletion (2007)
Conference Proceeding
Kovacs, T., & Bull, L. (2007). Toward a better understanding of rule initialisation and deletion. In H. Lipson (Ed.), Proceedings of the 2007 GECCO Conference on Genetic and Evolutionary Computation (2777-2780). https://doi.org/10.1145/1274000.1274060

A number of heuristics have been used in Learning Classifier Systems to initialise parameters of new rules, to adjust fitness of parent rules when they generate offspring, and to select rules for deletion. Some have not been studied in the literature... Read More about Toward a better understanding of rule initialisation and deletion.

Fuzzy-XCS: A Michigan genetic fuzzy system (2007)
Journal Article
Casillas, J., Carse, B., & Bull, L. (2007). Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems, 15(4), 536-550. https://doi.org/10.1109/TFUZZ.2007.900904

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform online reinforcement learning by means of Michig... Read More about Fuzzy-XCS: A Michigan genetic fuzzy system.

Learning classifier system ensembles with rule-sharing (2007)
Journal Article
Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163

This paper presents an investigation into exploiting the population-based nature of learning classifier systems (LCSs) for their use within highly parallel systems. In particular, the use of simple payoff and accuracy-based LCSs within the ensemble m... Read More about Learning classifier system ensembles with rule-sharing.