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Using XCS to describe continuous-valued problem spaces

Wyatt, David; Bull, Larry; Parmee, Ian


David Wyatt
Senior Lecturer in Games Technology

Lawrence Bull
AHOD Research and Scholarship and Prof

Ian Parmee


Tim Kovacs

Xavier Llorà

Keiki Takadama

Pier Luca Lanzi

Wolfgang Stolzmann

Stewart W. Wilson


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 search space characteristics, convex/non-convex regions of high performance and complex interplay between variables. In particular, two test environments are used to investigate the effects of different degrees of feature sampling, parameter sensitivity, training set size and rule subsumption. Results show that XCSR is able to deduce the characteristics of such problem spaces to a suitable level of accuracy. This paper provides a foundation for the possible use of XCS as an exploratory tool that can provide information from conceptual design spaces enabling a designer to identify the best direction for further investigation as well as a better representation of their design problem through redefinition and reformulation of the design space.

Start Date Jun 8, 2019
Publication Date Jan 1, 2007
Peer Reviewed Peer Reviewed
Volume 4399
Pages 308-332
Series Title Lecture Notes in Computer Science
Book Title Learning Classifier Systems
APA6 Citation Wyatt, D., Bull, L., & Parmee, I. (2007). Using XCS to describe continuous-valued problem spaces. In T. Kovacs, X. LlorĂ , K. Takadama, P. Luca Lanzi, W. Stolzmann, & S. W. Wilson (Eds.), Learning Classifier Systems, 308-332. Springer.
Keywords XCS,continuous-valued problem spaces
Publisher URL
Additional Information Title of Conference or Conference Proceedings : International Workshop on Learning Classifier Systems 2005