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

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