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Coevolving functions in genetic programming (2001)
Journal Article
Ahluwalia, M., & Bull, L. (2001). Coevolving functions in genetic programming. Journal of Systems Architecture, 47(7), 573-585. https://doi.org/10.1016/S1383-7621%2801%2900016-9

In this paper we introduce a new approach to the use of automatically defined functions (ADFs) within genetic programming. The technique consists of evolving a number of separate sub-populations of functions which can be used by a population of evolv... Read More about Coevolving functions in genetic programming.

Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks (2001)
Journal Article
Bull, L. (2001). Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks. Lecture Notes in Artificial Intelligence, 1996, 29-36

Abstract Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule discovery. This paper presents a simple Markov model of the algorithm in such systems, with the aim of examining the effects of different types of interdependence be... Read More about Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks.

A self-adaptive classifier system (2001)
Conference Proceeding
Hurst, J., & Bull, L. (2001). A self-adaptive classifier system. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.), Advances in Learning Classifier Systems. , (70-79). https://doi.org/10.1007/3-540-44640-0_6

© Springer-Verlag Berlin Heidelberg 2001. The use and benefits of self-adaptive parameters, particularly mutation, are well-known within evolutionary computing. In this paper we examine the use of parameter self-adaptation in Michigan-style Classifie... Read More about A self-adaptive classifier system.

A communication architecture for multi-agent learning systems (2000)
Book Chapter
Ireson, N., Cao, Y., Bull, L., & Miles, R. (2000). A communication architecture for multi-agent learning systems. In S. Cagnoni, R. Poli, G. D. Smith, D. Corne, M. Oates, E. Hart, …T. C. Fogarty (Eds.), Real-World Applications of Evolutionary Computing: EvoWorkshops 2000 (119-147). Springer

Self-adaptive mutation in ZCS controllers (2000)
Journal Article
Bull, L., & Hurst, J. (2000). Self-adaptive mutation in ZCS controllers. Lecture Notes in Artificial Intelligence, 1803, 339-346. https://doi.org/10.1007/3-540-45561-2_33

© Springer-Verlag Berlin Heidelberg 2000. The use and benefits of self-adaptive mutation operators are well-known within evolutionary computing. In this paper we examine the use of self-adaptive mutation in Michigan-style Classifier Systems with the... Read More about Self-adaptive mutation in ZCS controllers.

Self-adaptive mutation in classifier system controllers (2000)
Book Chapter
Bull, L., Hurst, J., & Tomlinson, A. (2000). Self-adaptive mutation in classifier system controllers. In J. Meyer, A. Berthoz, D. Floreano, H. L. Roitblat, & S. W. Wilson (Eds.), From Animals to Animats 6 (460-467). MIT Press

Distributed learning control of traffic signals (2000)
Journal Article
Bull, L., Cao, Y. J., Ireson, N., Bull, L., & Miles, R. (2000). Distributed learning control of traffic signals. Lecture Notes in Artificial Intelligence, 1803, 117-126. https://doi.org/10.1007/3-540-45561-2_12

© Springer-Verlag Berlin Heidelberg 2000. This paper presents a distributed learning control strategy for traffic signals. The strategy uses a fully distributed architecture in which there is effectively only one (low) level of control. Such strategy... Read More about Distributed learning control of traffic signals.

A zeroth level corporate classifier system (1999)
Presentation / Conference
Tomlinson, A., & Bull, L. (1999, June). A zeroth level corporate classifier system. Paper presented at Genetic and Evolutionary Computation Conference (GECCO’99)

A genetic programming-based classifier system (1999)
Presentation / Conference
Ahluwalia, M., Bull, L., & Banzhaf, W. (1999, June). A genetic programming-based classifier system. Paper presented at Genetic and Evolutionary Computation Conference (GECCO-99)