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

Bull, Larry; Lawson, I.; Adamatzky, Andrew; de Lacy Costello, Ben

Abstract

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

Citation

Bull, L., Lawson, I., Adamatzky, A., & de Lacy Costello, B. (2005, September). Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata. Paper presented at IEEE Congress on Evolutionary Computation, 2005

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: IEEE Congress on Evolutionary Computation, 2005
• Start Date: Sep 2, 2005
• End Date: Sep 4, 2005
• Publication Date: Sep 5, 2005
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Pages: 136-141
• Keywords: cellular automata, data mining, evolutionary computation, learning, artificial intelligence, pattern classification
• Publisher URL: http://www.ieee.org/index.html
• Related Public URLs: 10.1109/CEC.2005.1554677
• Additional Information: Title of Conference or Conference Proceedings : Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005