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: (2007) Learning Classifier Systems. (4399) Springer, pp. 308-332. ISBN 9783540712305 Available from: http://eprints.uwe.ac.uk/20672

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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:artificial intelligence, computation, abstract devices, XCS
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20672
Deposited By: M. Clarke
Deposited On:25 Jul 2013 11:23
Last Modified:15 Nov 2016 22:20

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