Robotic experimentation using constructivism within a neural learning classifier system
(2003)
Report
Hurst, J., & Bull, L. (2003). Robotic experimentation using constructivism within a neural learning classifier system
Feature construction and selection using genetic programming and a genetic algorithm (2003)
Journal Article
Smith, M. G., & Bull, L. (2003). Feature construction and selection using genetic programming and a genetic algorithm. Lecture Notes in Artificial Intelligence, 2610, 229-237. https://doi.org/10.1007/3-540-36599-0_21The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified usin... Read More about Feature construction and selection using genetic programming and a genetic algorithm.
Comparing learning classifier systems for continuous-valued online environments (2003)
Journal Article
Stone, C., & Bull, L. (2003). Comparing learning classifier systems for continuous-valued online environmentsWe investigate Learning Classifier Systems for online environments that consist of real-valued states and which require every action made by the agent to count towards its performance. Two Learning Classifier System architectures are considered, ZCS... Read More about Comparing learning classifier systems for continuous-valued online environments.
For Real! XCS with Continuous-Valued Inputs (2003)
Journal Article
Stone, C., & Bull, L. (2003). For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation, 11(3), 299-336. https://doi.org/10.1162/106365603322365315Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations... Read More about For Real! XCS with Continuous-Valued Inputs.
Towards learning classifier systems for continuous-valued online environments (2003)
Book Chapter
Stone, C., & Bull, L. (2003). Towards learning classifier systems for continuous-valued online environments. In E. Cantu-Paz (Ed.), GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: Part II (209-209). Springer
Addressing policy objectives of traffic control using evolutionary algorithms (2002)
Presentation / Conference
Sha'Aban, J., Tomlinson, A., HEYDECKER, B., & Bull, L. (2002, September). Addressing policy objectives of traffic control using evolutionary algorithms. Paper presented at European Transport Conference 2002, Cambridge, UKThis paper presents preliminary results from an ongoing research project that is investigating traffic management and signal control of evolutionary algorithms that use machine learning. It describes the application of novel technologies of machine l... Read More about Addressing policy objectives of traffic control using evolutionary algorithms.
Towards the use of XCS in interactive evolutionary design (2002)
Book Chapter
Bull, L., Wyatt, D., & Parmee, I. (2002). Towards the use of XCS in interactive evolutionary design. In W. Langdon (Ed.), GECCO-2002: proceedings of the Genetic and Evolutionary Computation Conference (951). Morgan Kaufmann Publishers
An investigation into island model rule migration for a number of mobile autonomous learning classifier system agents (2002)
Presentation / Conference
Wyatt, D., & Bull, L. (2002, July). An investigation into island model rule migration for a number of mobile autonomous learning classifier system agents. Paper presented at Genetic and Evolutionary Computation Conference, New York, 2002, New York
Adaptive traffic control using evolutionary algorithms (2002)
Presentation / Conference
Sha'Aban, J., Tomlinson, A., Heydecker, B. G., & Bull, L. (2002, June). Adaptive traffic control using evolutionary algorithms. Paper presented at 13th Mini-Euro Conference, Bari, Italy
Lookahead and latent learning in ZCS (2002)
Presentation / Conference
Bull, L. (2002, June). Lookahead and latent learning in ZCS. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference 2002
Learning classifier systems (2002)
Journal Article
Bull, L., Lanzi, P., & Stolzmann, W. (2002). Learning classifier systems. Soft Computing, 6(3-4), 143-143. https://doi.org/10.1007/s005000100110
ZCS redux (2002)
Journal Article
Bull, L., & Hurst, J. (2002). ZCS redux. Evolutionary Computation, 10(2), 185-205. https://doi.org/10.1162/106365602320169848Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payof... Read More about ZCS redux.
On accuracy-based fitness (2002)
Journal Article
Bull, L. (2002). On accuracy-based fitness. Soft Computing, 6(3), 154-161. https://doi.org/10.1007/s005000100112
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_54For 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.
I theory-a self-adaptive XCS (2002)
Journal Article
Hurst, J., & Bull, L. (2002). I theory-a self-adaptive XCS. Lecture Notes in Artificial Intelligence, 2321, 57-73
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_5Self-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.
TCS learning classifier system controller on a real robot (2002)
Journal Article
Hurst, J., Bull, L., & Melhuish, C. (2002). TCS learning classifier system controller on a real robot. Lecture Notes in Artificial Intelligence, 2439, 588-597. https://doi.org/10.1007/3-540-45712-7_57To date there have been few implementation of Holland’s Learning Classifier System (LCS) on real robots. The paper introduces a Temporal Classifier System (TCS), an LCS derived from Wilson’s ZCS. Traditional LCS have the ability to generalise over th... Read More about TCS learning classifier system controller on a real robot.
ZCS and TCS learning classifier system controllers on real robots (2002)
Report
Hurst, J., Bull, L., & Melhuish, C. (2002). ZCS and TCS learning classifier system controllers on real robots
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