Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Reinforcement Learning
Computing in nonlinear media and automata collectives
Computing in nonlinear media and automata collectives
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
For real! XCS with continuous-valued inputs
Evolutionary Computation
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Grand challenge 7: journeys in non-classical computation
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
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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 a checkerboard 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 properties 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.