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All Outputs (8)

Dynamic control and information processing in the Belousov-Zhabotinsky reaction using a coevolutionary algorithm (2008)
Journal Article
Toth, R., Stone, C., Adamatzky, A., De Lacy Costello, B., & Bull, L. (2008). Dynamic control and information processing in the Belousov-Zhabotinsky reaction using a coevolutionary algorithm. Journal of Chemical Physics, 129(18), 184708. https://doi.org/10.1063/1.2932252

We propose that the behavior of nonlinear media can be controlled dynamically through coevolutionary systems. In this study, a light-sensitive subexcitable Belousov-Zhabotinsky reaction is controlled using a heterogeneous cellular automaton. A checke... Read More about Dynamic control and information processing in the Belousov-Zhabotinsky reaction using a coevolutionary algorithm.

Coevolving cellular automata with memory for chemical computing: Boolean logic gates in the BZ reaction (2008)
Book Chapter
Stone, C., Toth, R., de Lacy Costello, B., Bull, L., & Adamatzky, A. (2008). Coevolving cellular automata with memory for chemical computing: Boolean logic gates in the BZ reaction. In G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, & N. Beume (Eds.), Parallel Problem Solving from Nature - PPSN X: 10th International Conference Dortmund, Germany, September 13-17, 2008 (579-588). Berlin/Heidelberg: Springer

Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system (2008)
Journal Article
Bull, L., Budd, A., Stone, C., Uroukov, I., Costello, B. D. L., & Adamatzky, A. (2008). Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system. Artificial Life, 14(2), 203-222. https://doi.org/10.1162/artl.2008.14.2.203

We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this... Read More about Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system.

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

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 realise... Read More about Initial results from the use of evolutionary learning to control chemical computers.

Non-linear media based computers: chemical and neuronal networks through machine learning (2008)
Report
Bull, L., Adamatzky, A., de Lacy Costello, B., Husbands, P., O’Shea, M., Purcell, W., & Taylor, A. (2008). Non-linear media based computers: chemical and neuronal networks through machine learning

There is growing interest in research into the development of hybrid wetware-silicon devices focused on exploiting their potential for 'non-linear computing'. The aim is to harness the as yet only partially understood intricate dynamics of non-linear... Read More about Non-linear media based computers: chemical and neuronal networks through machine learning.