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Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system

Bull, Larry; Budd, Adam; Stone, Christopher; Uroukov, Ivan; Costello, Ben de Lacy; Adamatzky, Andrew

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Authors

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor

Adam Budd

Christopher Stone

Ivan Uroukov



Abstract

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 initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which archeckerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properittes of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments. © 2008 Massachusetts Institute of Technology.

Citation

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

Journal Article Type Article
Publication Date Mar 1, 2008
Deposit Date Jul 23, 2010
Publicly Available Date Feb 9, 2016
Journal Artificial Life
Print ISSN 1064-5462
Electronic ISSN 1530-9185
Publisher Massachusetts Institute of Technology Press (MIT Press)
Peer Reviewed Peer Reviewed
Volume 14
Issue 2
Pages 203-222
DOI https://doi.org/10.1162/artl.2008.14.2.203
Keywords unconventional computing, simulated evolution, nonlinear media
Public URL https://uwe-repository.worktribe.com/output/1017362
Publisher URL http://dx.doi.org/10.1162/artl.2008.14.2.203