All Outputs (15)

Coevolutionary species adaptation genetic algorithms: A continuing SAGA on coupled fitness landscapes (2005)
Journal Article
Bull, L. (2005). Coevolutionary species adaptation genetic algorithms: A continuing SAGA on coupled fitness landscapes. Lecture Notes in Artificial Intelligence, 3630 LNAI, 322-331. https://doi.org/10.1007/11553090_33

The Species Adaptation Genetic Algorithm (SAGA) was introduced to facilitate the open-ended evolution of artificial systems. The approach enables genotypes to increase in length through appropriate mutation operators and has been successfully exploit... Read More about Coevolutionary species adaptation genetic algorithms: A continuing SAGA on coupled fitness landscapes.

X-TCS: Accuracy-based learning classifier system robotics (2005)
Journal Article
Studley, M., & Bull, L. (2005). X-TCS: Accuracy-based learning classifier system robotics. https://doi.org/10.1109/CEC.2005.1554954

Although most learning classifier system (LCS) research uses the accuracy-based XCS, it had never been used to control a physical robot before. In comparison to purely evolutionary or purely reinforcement learning approaches, an LCS should be faster... Read More about X-TCS: Accuracy-based learning classifier system robotics.

Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata (2005)
Presentation / Conference
Bull, L., Lawson, I., Adamatzky, A., & de Lacy Costello, B. (2005, September). Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata. Paper presented at IEEE Congress on Evolutionary Computation, 2005, Edinburgh, UK

This paper presents a novel approach to the programming of automata-based simulation and computation using a machine learning technique. The identification of lattice-based automata for real-world applications is cast as a data mining problem. Our ap... Read More about Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata.

Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network (2005)
Book Chapter
O'Hara, T., & Bull, L. (2005). Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network. In T. O'Hara, & L. Bull (Eds.), The 2005 IEEE Congress on Evolutionary Computation (2046-2052). IEEE. https://doi.org/10.1109/CEC.2005.1554947

Coevolutionary species adaptation genetic algorithms: growth and mutation on coupled fitness landscapes (2005)
Presentation / Conference
Bull, L. (2005, September). Coevolutionary species adaptation genetic algorithms: growth and mutation on coupled fitness landscapes. Paper presented at 2005 IEEE Congress on Eolutionary Computation, Edinburgh, UK

The species adaptation genetic algorithm (SAGA) was introduced to facilitate the open-ended evolution of artificial systems. The approach enables genotypes to increase in length through appropriate mutation operators. Most recently, this has been und... Read More about Coevolutionary species adaptation genetic algorithms: growth and mutation on coupled fitness landscapes.

Genetic programming with a genetic algorithm for feature construction and selection (2005)
Journal Article
Smith, M. G., & Bull, L. (2005). Genetic programming with a genetic algorithm for feature construction and selection. Genetic Programming and Evolvable Machines, 6(3), 265-281. https://doi.org/10.1007/s10710-005-2988-7

The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we primarily examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is class... Read More about Genetic programming with a genetic algorithm for feature construction and selection.

An analysis of continuous-valued representations for learning classifier systems (2005)
Book Chapter
Stone, C., & Bull, L. (2005). An analysis of continuous-valued representations for learning classifier systems. In L. Bull, & T. Kovacs (Eds.), Foundations of learning classifier systems (127-178). Springer

On the use of rule-sharing in learning classifier system ensembles (2005)
Presentation / Conference
Bull, L., Studley, M., Bagnall, T., & Whittley, I. (2005, June). On the use of rule-sharing in learning classifier system ensembles. Paper presented at The 2005 IEEE Congress on Evolutionary Computation

Revisiting genetic selection in the XCS learning classifier system (2005)
Presentation / Conference
Kharbat, F., Bull, L., & Odeh, M. (2005, June). Revisiting genetic selection in the XCS learning classifier system. Paper presented at The 2005 IEEE Congress on Evolutionary Computation

A memetic accuracy-based neural learning classifier system (2005)
Presentation / Conference
O'Hara, T., & Bull, L. (2005, June). A memetic accuracy-based neural learning classifier system. Paper presented at The 2005 IEEE Congress on Evolutionary Computation

Attribute selection methods for filtered attribute subspace based bagging with injected randomness (FASBIR) (2005)
Presentation / Conference
Whittley, I., Bagnall, A., Bull, L., Pettipher, M., Studley, M., & Tekiner, F. (2005, April). Attribute selection methods for filtered attribute subspace based bagging with injected randomness (FASBIR). Paper presented at 2005 SIAM International Conference on Data Mining, Newport Beach, California

Two simple learning classifier systems (2005)
Book Chapter
Bull, L. (2005). Two simple learning classifier systems. In L. Bull, & T. Kovacs (Eds.), Foundations of Learning Classifier Systems (913-913). Springer. https://doi.org/10.1007/11319122_4

Foundations of learning classifier systems: An introduction (2005)
Book Chapter
Bull, L., & Kovacs, T. (2005). Foundations of learning classifier systems: An introduction. In L. Bull, & T. Kovacs (Eds.), Foundations of Learning Classifier Systems (1-17). Springer. https://doi.org/10.1007/11319122_1

Comparing XCS and ZCS on noisy continuous valued environments (2005)
Report
Stone, C., & Bull, L. (2005). Comparing XCS and ZCS on noisy continuous valued environments

A memetic learning classifier system for describing continuous-valued problem spaces (2005)
Book Chapter
Wyatt, D., & Bull, L. (2005). A memetic learning classifier system for describing continuous-valued problem spaces. In W. E. Hart, N. Krasnogor, & J. Smith (Eds.), Recent advances in memetic algorithms (355-395). Springer. https://doi.org/10.1007/3-540-32363-5_15