Initial Results from the Use of Evolutionary Learning to Control Chemical Computers
Adam Budd, Christopher Stone, Jonathan Masere, Andrew Adamatzky, Ben De Lacy Costello and Larry Bull
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.