Initial results from the use of evolutionary learning to control chemical computers

Budd, A., Stone, C., Masere, J., Adamatzky, A., de Lacy Costello, B. and Bull, L. (2008) Initial results from the use of evolutionary learning to control chemical computers. International Journal of Unconventional Computing, 4 (1). pp. 13-22. ISSN 1548-7199 Available from:

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The behaviour of pulses of Belousov-Zhabotinski (BZ) reaction diffusion waves can be controlled automatically through machine learning. By extension, a form of chemical network computing, i.e., a massively parallel non-linear Computer, can be realised by Such an approach. In this initial study a light-sensitive sub-excitable BZ reaction in which a checkerboard image comprising of varying light intensity cells is projected onto the Surface of a thin silica gel impregnated with tris(bipyridyl) ruthenium (II) catalyst and indicator is used to make the network. As a catalyst free BZ solution is swept past the gel, Pulses of wave fragments are injected into the checkerboard grid resulting in rich spatio-temporal behaviour. An evolutionary computing machine learning approach, a learning classifier system, is then shown able to direct the fragments through dynamic control of the light intensity within each cell in both simulated and real chemical systems.

Item Type:Article
Uncontrolled Keywords:systems. sensitivity, behaviour, computer science, theory and methods
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20681
Deposited By: M. Clarke
Deposited On:25 Jul 2013 11:06
Last Modified:05 Apr 2016 02:24

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