Christopher Stone
For Real! XCS with Continuous-Valued Inputs
Stone, Christopher; Bull, Larry
Abstract
Many 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 recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other Learning Classifier System architectures. We discuss limitations of the real multiplexer problem, a benchmark problem used for Learning Classifier Systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer. Representations and operators are compared, using both the real multiplexer and checkerboard problems and we find that representational, operator and sampling bias all affect the performance of XCS in continuous-valued environments.
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 2003 |
Deposit Date | Jan 22, 2010 |
Publicly Available Date | Nov 15, 2016 |
Journal | Evolutionary Computation |
Print ISSN | 1063-6560 |
Electronic ISSN | 1530-9304 |
Publisher | Massachusetts Institute of Technology Press (MIT Press) |
Peer Reviewed | Peer Reviewed |
Volume | 11 |
Issue | 3 |
Pages | 299-336 |
DOI | https://doi.org/10.1162/106365603322365315 |
Keywords | XCS, continuous-valued inputs |
Public URL | https://uwe-repository.worktribe.com/output/1074808 |
Publisher URL | http://dx.doi.org/10.1162/106365603322365315 |
Related Public URLs | http://www.mitpressjournals.org/toc/evco/11/3 |
Additional Information | Additional Information : Since they can be used to evolve traditional production system rules, Learning Classifier Systems have proven useful tools for data mining and knowledge discovery. This paper explores the most efficient way by which to represent rules consisting of vectors of real numbers, both formally and experimentally, as this is perhaps the most typical form of real-world data sets. The paper presents a new representation which has been widely adopted by the users of these systems. As a consequence, an EPSRC project (GR/T18455/01) to create super-computer implementations of such systems and other machine learning techniques was obtained - the Super Computer Data Mining Toolkit hosted by the AI Group. This is currently being used by the Group to explore Olympic athlete data (EP/43488/01), breast cancer data for a local health trust, bowling technique for the English Cricket Board, and the system identification of complex systems considering memory (EP/E042981/01). Copyright of this article is (c) MIT Press, 2003. |
Contract Date | Nov 15, 2016 |
Files
106365603322365315.pdf
(1.1 Mb)
PDF
You might also like
Towards the evolution of vertical-axis wind turbines using supershapes
(2014)
Journal Article
Evolving unipolar memristor spiking neural networks
(2015)
Journal Article
Evolving functional and structural dynamism in coupled boolean networks
(2014)
Journal Article
A brief history of learning classifier systems: from CS-1 to XCS and its variants
(2015)
Journal Article
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
(2013)
Journal Article
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search