Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata

Bull, L., Lawson, I., Adamatzky, A. and de Lacy Costello, B. (2005) Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, Edinburgh, UK, 2nd - 4th September, 2005. US: Institute of Electrical and Electronics Engineers (IEEE), pp. 136-141 Available from: http://eprints.uwe.ac.uk/20715

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Abstract/Description

This paper presents a novel approach to the programming of automata-based simulation and computation using a machine learning technique. The identification of lattice-based automata for real-world applications is cast as a data mining problem. Our approach to achieving this is to use evolutionary computing and reinforcement learning with performance fed back indicating the predictive accuracy of future behaviour of the given system. The purpose of this work is to develop an approach to identifying automata rules that can achieve good performance using data from a variety of kinds of complex systems

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:cellular automata, data mining, evolutionary computation, learning, artificial intelligence, pattern classification
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20715
Deposited By: M. Clarke
Deposited On:17 Jul 2013 10:31
Last Modified:28 Mar 2016 16:55

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