The use of a minimal number of vapour sensors for the assessment of food quality (2001)
Book Chapter
de Lacy Costello, B., Ewen, R., Gunson, H., Ratcliffe, N. M., & Spencer-Phillips, P. T. (2001). The use of a minimal number of vapour sensors for the assessment of food quality. In S. Clark, K. Thompson, C. Keevil, & M. Smith (Eds.), Rapid Detection Assays (148-152). Royal Society of Chemistry

The detection of harmful chemicals and microbial pathogens in food and water destined for consumers is of paramount importance the world over and it is vital that new techniques and discoveries are widely disseminated. Bringing together international... Read More about The use of a minimal number of vapour sensors for the assessment of food quality.

Gas chromatography-mass spectrometry analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum (2001)
Journal Article
Spencer-Phillips, P. T., Ratcliffe, N. M., Jones, P. R., De Lacy Costello, B. P., de Lacy Costello, B., Evans, P., …Spencer-Phillips, P. T. (2001). Gas chromatography-mass spectrometry analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum. Plant Pathology, 50(4), 489-496. https://doi.org/10.1046/j.1365-3059.2001.00594.x

Volatile organic compounds (VOCs) collected from potato tubers inoculated with Phytophthora infestans (late blight), Fusarium coeruleum (dry rot) or sterilized distilled water (as a control) were analysed using gas chromatography-mass spectrometry (G... Read More about Gas chromatography-mass spectrometry analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum.

Simple models of coevolutionary genetic algorithms (2001)
Journal Article
Bull, L. (2001). Simple models of coevolutionary genetic algorithms. Artificial Life and Robotics, 5(1), 58-66. https://doi.org/10.1007/BF02481321

The use of evolutionary computing techniques in coevolutionary/multiagent systems is becoming increasingly popular. This paper presents some simple models of the genetic algorithm in such systems, with the aim of examining the effects of different ty... Read More about Simple models of coevolutionary genetic algorithms.

An investigation into LCS road traffic junction controllers (2001)
Journal Article
Tomlinson, A., & Bull, L. (2001). An investigation into LCS road traffic junction controllers

ATLAS: Collisions of gliders as phases of ether in rule 110 (2001)
Working Paper
Martinez, G. J., & McIntosh, H. V. ATLAS: Collisions of gliders as phases of ether in rule 110. México

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.

Self-adaptation in classifier system controllers (2001)
Journal Article
Hurst, J., & Bull, L. (2001). Self-adaptation in classifier system controllers. Artificial Life and Robotics, 5(2), 109-119. https://doi.org/10.1007/BF02481348

An accuracy-based neural classifier system (2001)
Report
Bull, L., & O’Hara, T. (2001). An accuracy-based neural classifier system

Learning Classifier Systems have traditionally used a binary representation, with wildcards added to facilitate generalization. As they are applied to more complex domains the simple representation can become limiting. In this paper we present result... Read More about An accuracy-based neural classifier system.

ZCS: Theory and practice: UWE Learning Classifier Systems Group Technical Report 01 (2001)
Report
Bull, L., & Hurst, J. (2001). ZCS: Theory and practice: UWE Learning Classifier Systems Group Technical Report 01

Simple markov models of the genetic algorithm in classifier systems: Multi-step tasks (2001)
Book Chapter
Bull, L. (2001). Simple markov models of the genetic algorithm in classifier systems: Multi-step tasks. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.), Advances in Learning Classifier Systems (299-306). Springer

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.

On coevolutionary genetic algorithms (2001)
Journal Article
Bull, L. (2001). On coevolutionary genetic algorithms. Soft Computing, 5(3), 201-207. https://doi.org/10.1007/s005000100082

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.

Computing in nonlinear media and automata collectives (2001)
Book
Adamatzky, A. (2001). Computing in nonlinear media and automata collectives. Bristol: Institute of Physics Publishing

Symbiogenesis in learning classifier systems (2001)
Journal Article
Tomlinson, A., & Bull, L. (2001). Symbiogenesis in learning classifier systems. Artificial Life, 7(1), 33-61. https://doi.org/10.1162/106454601300328016