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All Outputs (14)

Fuzzy dynamical genetic programming in XCSF (2011)
Conference Proceeding
Preen, R., & Bull, L. (2011). Fuzzy dynamical genetic programming in XCSF. In N. Krasnogor, & P. L. Lanzi (Eds.), Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. , (167-168). https://doi.org/10.1145/2001858.2001952

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigat... Read More about Fuzzy dynamical genetic programming in XCSF.

A spiking neural representation for XCSF (2010)
Conference Proceeding
Lanzi, P. L., Howard, G., Howard, D., & Bull, L. (2010). A spiking neural representation for XCSF. https://doi.org/10.1109/CEC.2010.5586035

This paper presents a Learning Classifier System (LCS) where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. The evolutionary design process exploits parameter self-adaptation and a con... Read More about A spiking neural representation for XCSF.

On lookahead and latent learning in simple LCS (2008)
Conference Proceeding
Bull, L. (2008). On lookahead and latent learning in simple LCS. https://doi.org/10.1007/978-3-540-88138-4_9

Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of an accuracy-based scheme wher... Read More about On lookahead and latent learning in simple LCS.

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.

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.

On using constructivism in neural classifier systems (2002)
Conference Proceeding
Bull, L. (2002). On using constructivism in neural classifier systems. In J. J. Merelo, P. Adamidis, & H. Beyer (Eds.), In Parallel Problem Solving from Nature — PPSN VII. , (558-567). https://doi.org/10.1007/3-540-45712-7_54

For artificial entities to achieve true autonomy and display complex life-like behaviour they will need to exploit appropriate adaptable learning algorithms. In this sense adaptability implies flexibility guided by the environment at any given time a... Read More about On using constructivism in neural classifier systems.

Consideration of multiple objectives in neural learning classifier systems (2002)
Conference Proceeding
Bull, L., & Studley, M. (2002). Consideration of multiple objectives in neural learning classifier systems. In J. J. Merelo, P. Adamidis, & H. Beyer (Eds.), Parallel Problem Solving from Nature—PPSN VII. , (549-557). https://doi.org/10.1007/3-540-45712-7_53

© Springer-Verlag Berlin Heidelberg 2002. For effective use in a number of problem domains Learning Classifier Systems must be able to manage multiple objectives. This paper explicitly considers the case of developing the controller for a simulated m... Read More about Consideration of multiple objectives in neural learning classifier systems.

Initial modifications to XCS for use in interactive evolutionary design (2002)
Conference Proceeding
Bull, L., Wyatt, D., & Parmee, I. (2002). Initial modifications to XCS for use in interactive evolutionary design. In J. J. Merelo, P. Adamidis, & H. Beyer (Eds.), Parallel Problem Solving from Nature—PPSN VII. , (568-577). https://doi.org/10.1007/3-540-45712-7_55

© Springer-Verlag Berlin Heidelberg 2002. Learning classifier systems represent a technique by which various characteristics of a given problem space may be deduced and presented to the user in a readable format. In this paper we present results from... Read More about Initial modifications to XCS for use in interactive evolutionary design.

A self-adaptive XCS (2002)
Conference Proceeding
Hurst, J., & Bull, L. (2002). A self-adaptive XCS. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.), In Advances in Learning Classifier Systems. , (57-73). https://doi.org/10.1007/3-540-48104-4_5

Self-adaptation has been used extensively to control parameters in various forms of evolutionary computation. The concept was first introduced with evolutionary strategies and it is now often used to control genetic algorithms. This paper describes t... Read More about A self-adaptive XCS.

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.

Evolutionary computing in multi-agent environments: Operators (1998)
Conference Proceeding
Bull, L. (1998). Evolutionary computing in multi-agent environments: Operators. In V. W. Porto, N. Saravanan, D. Waagen, & A. E. Eiben (Eds.), In EP 1998: Evolutionary Programming VII. , (43-52). https://doi.org/10.1007/BFb0040758

This paper examines a key aspect of applying evolutionary computing techniques to multi-agent systems: a comparison in the performance of the genetic operators of mutation and recombination. Using the tuneable NKC model of multi-agent evolution it is... Read More about Evolutionary computing in multi-agent environments: Operators.

On ZCS in multi-agent environments (1998)
Conference Proceeding
Bull, L. (1998). On ZCS in multi-agent environments. In A. Eiben, T. Bäck, M. Schoenauer, & H. Schwefel (Eds.), Parallel Problem Solving from Nature—PPSN V. , (471-480). https://doi.org/10.1007/BFb0056889

This paper examines the performance of the ZCS Michigan-style classifier system in multi-agent environments. Using an abstract multi-agent model the effects of varying aspects of the performance, reinforcement and discovery components are examined. I... Read More about On ZCS in multi-agent environments.

A corporate classifier system (1998)
Conference Proceeding
Tomlinson, A., & Bull, L. (1998). A corporate classifier system. In A. E. Eiben, T. Bäck, M. Schoenauer, & H. Schwefel (Eds.), Parallel Problem Solving from Nature—PPSN V. , (550-559). https://doi.org/10.1007/BFb0056897

Based on the proposals of Wilson and Goldberg we introduce a macro-level evolutionary operator which creates structural links between rules in the ZCS model and thus forms "corporations" of rules within the classifier system population. Rule co-depen... Read More about A corporate classifier system.

An evolution strategy and genetic algorithm hybrid: An initial implementation and first results (1994)
Conference Proceeding
Bull, L., & Fogarty, T. C. (1994). An evolution strategy and genetic algorithm hybrid: An initial implementation and first results. In T. C. Fogarty (Ed.), In AISB EC 1994: Evolutionary Computing. , (95-102). https://doi.org/10.1007/3-540-58483-8_8

Evolution Strategies (ESs)[15] and Genetic Algorithms (GAs)[13] have both been used to optimise functions, using the natural process of evolution as inspiration for their search mechanisms. The ES uses gene mutation as it’s main search operator whils... Read More about An evolution strategy and genetic algorithm hybrid: An initial implementation and first results.