Jacob Hurst
A self-adaptive classifier system
Hurst, Jacob; Bull, Larry
Authors
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Contributors
Pier L. Lanzi
Editor
Wolfgang Stolzmann
Editor
Stewart W. Wilson
Editor
Abstract
© 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 Classifier Systems with the aim of improving their performance and ease of use. We implement a fully self-adaptive ZCS classifier and examine its performance in a multi-step environment. It is shown that the mutation rate, learning rate, discount factor and tax rate can be developed along with an appropriate solution/rule-base, resulting in improved performance over results using fixed rate parameters. We go on to show that the benefits of self-adaptation are particularly marked in non-stationary environments.
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | Advances in Learning Classifier Systems |
Start Date | Sep 15, 2000 |
End Date | Sep 16, 2000 |
Publication Date | Jan 1, 2001 |
Publisher | Springer Verlag |
Peer Reviewed | Not Peer Reviewed |
Pages | 70-79 |
Series Title | Lecture Notes in Computer Science |
Series Number | 1996 |
Series ISSN | 0302-9743 |
Book Title | Advances in Learning Classifier Systems |
ISBN | 9783540424376 |
DOI | https://doi.org/10.1007/3-540-44640-0_6 |
Keywords | artificial intelligence, mathematical logic and formal languages, computation by abstract devices |
Public URL | https://uwe-repository.worktribe.com/output/1091270 |
Publisher URL | http://dx.doi.org/10.1007/3-540-44640-0_6 |
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