Using XCS to describe continuous-valued problem spaces

Wyatt, D., Bull, L. and Parmee, I. (2007) Using XCS to describe continuous-valued problem spaces. In: Kovacs, T., Llorà, X., Takadama, K., Luca Lanzi, P., Stolzmann, W. and Wilson, S. W., eds. (2007) Learning Classifier Systems. (4399) Springer, pp. 308-332. Available from: http://eprints.uwe.ac.uk/22545

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Publisher's URL: http://dx.doi.org/10.1007/978-3-540-71231-2_21

Abstract/Description

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.

Item Type:Book Section
Uncontrolled Keywords:XCS,continuous-valued problem spaces
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:22545
Deposited By: Dr D. Wyatt
Deposited On:06 Mar 2014 17:01
Last Modified:15 Nov 2016 22:21

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