Genetic optimization of cellular neural networks

The operation of a cellular neural network (CNN) is defined by a set of 19 parameters. There is no known general method for finding these parameters; analytic design methods are available for a small class of problems only. Standard learning algorithms cannot be applied due to the lack of gradient information. In this paper, we propose a genetic algorithm as a generally applicable global learning method. In order to be useful for real CNN VLSI chips, the parameters have to be insensitive to small perturbations. Therefore, after the parameters are learnt, they are optimized with respect to robustness in a second genetic processing step. As the simulation of CNNs necessitates the numerical integration of large systems of nonlinear differential equations, the evaluation of the fitness functions is computationally very expensive; a massively parallel super-computer is used to achieve acceptable run times.