Outputs (20)

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.

A genetic approach to search for glider guns in cellular automata (2007)
Presentation / Conference
Sapin, E., Bull, L., & Adamatzky, A. (2007, September). A genetic approach to search for glider guns in cellular automata. Paper presented at Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, Singapore

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.

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.

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.

Improving the human readability of features constructed by genetic programming (2007)
Presentation / Conference
Smith, M., & Bull, L. (2007, July). Improving the human readability of features constructed by genetic programming. Paper presented at Proceedings of the 9th annual conference on Genetic and evolutionary computation, London, England

Towards the coevolution of cellular automata controllers for chemical computing with the B-Z reaction (2007)
Presentation / Conference
Stone, C., Toth, R., Adamatzky, A., de Lacy Costello, B., & Bull, L. (2007, July). Towards the coevolution of cellular automata controllers for chemical computing with the B-Z reaction. Paper presented at 9th annual conference on Genetic and evolutionary computation, London, England

Mining breast cancer data with XCS (2007)
Presentation / Conference
Kharbat, F., Bull, L., & Odeh, M. (2007, June). Mining breast cancer data with XCS. Paper presented at Proceedings of the 9th annual conference on Genetic and evolutionary computation

Towards clustering with XCS (2007)
Presentation / Conference
Tamee, K., Bull, L., & Pinngern, O. (2007, June). Towards clustering with XCS. Paper presented at Proceedings of the 9th annual conference on Genetic and evolutionary computation

Ycsc: a modified clustering technique based on lcs (2007)
Journal Article
Tamee, K., Bull, L., & Pinngern, O. (2007). Ycsc: a modified clustering technique based on lcs. Journal of Digital Information Management, 5(3), 160-166

This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System with a modification to the original YCS fitness function has been found to improve the identification of less-separated data sets. Our approac... Read More about Ycsc: a modified clustering technique based on lcs.

Unconventional computing 2007 (2007)
Book
Adamatzky, A., de Lacy Costello, B., Bull, L., Stepney, S., & Teuscher, C. (2007). Unconventional computing 2007. Frome: Luniver Press

2007 index IEEE transactions on evolutionary computation vol. 11 (2007)
Journal Article
Aguilar-Ruiz, J., Alfonseca, M., Alonso, J., Alvarruiz, F., Arnold, D., Baesens, B., …Bull, L. (2007). 2007 index IEEE transactions on evolutionary computation vol. 11. IEEE Transactions on Evolutionary Computation, 11(6), 791-796. https://doi.org/10.1109/TEVC.2007.913016

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also b... Read More about 2007 index IEEE transactions on evolutionary computation vol. 11.

Towards canonical learning classifier systems: Simple accuracy, payoff and anticipatory systems (2007)
Report
Bull, L. (2007). Towards canonical learning classifier systems: Simple accuracy, payoff and anticipatory systems

A learning classifier system approach to the identification of cellular automata (2007)
Journal Article
Bull, L., & Adamatzky, A. (2007). A learning classifier system approach to the identification of cellular automata. Journal of Cellular Automata, 2(1), 21-38

Towards unconventional computing through simulated evolution: Learning classifier system control of non-linear media (2007)
Journal Article
Bull, L., Budd, A., Stone, C., Uroukov, I. S., Costello, B. D. L., & Adamatzky, A. (2007). Towards unconventional computing through simulated evolution: Learning classifier system control of non-linear media. Artificial Life,

New approach for extracting knowledge from the XCS learning classifier system (2007)
Journal Article
Kharbat, F., Odeh, M., & Bull, L. (2007). New approach for extracting knowledge from the XCS learning classifier system. International Journal of Hybrid Intelligent Systems, 4(2), 49-62

Backpropagation in accuracy-based neural learning classifier systems (2007)
Book Chapter
O'Hara, T., & Bull, L. (2007). Backpropagation in accuracy-based neural learning classifier systems. In J. Bacardit, E. Bernadó-Mansilla, M. V. Butz, T. Kovacs, X. Llorà, & K. Takadama (Eds.), Learning Classifier Systems (25-39). Springer Verlag. https://doi.org/10.1007/978-3-540-71231-2_3

Learning Classifier Systems traditionally use a binary string rule representation with wildcards added to allow for generalizations over the problem encoding. We have presented a neural network-based representation to aid their use in complex problem... Read More about Backpropagation in accuracy-based neural learning classifier systems.

Using XCS to describe continuous-valued problem spaces (2007)
Conference Proceeding
Wyatt, D., Bull, L., & Parmee, I. (2007). Using XCS to describe continuous-valued problem spaces. https://doi.org/10.1007/978-3-540-71231-2_21

Learning classifier systems have previously been shown to have some application in single-step tasks. This paper extends work in the area by applying the classifier system to progressively more complex multi-modal test environments, each with typical... Read More about Using XCS to describe continuous-valued problem spaces.