Stochastic and hybrid approaches toward robust templates

We propose and compare different methods of synthesizing robust templates for cellular neural networks. In the first approach, genetic algorithms are used for both template learning and optimization with respect to robustness. The evaluation of the fitness functions in the optimization step is computationally very expensive; a massively parallel supercomputer is used to achieve acceptable run times. As alternative approaches, a steepest-ascent method and an averaging approach are presented, the latter being computationally inexpensive. To overcome their respective drawbacks, these algorithms are combined into a hybrid approach which is shown to be efficient even for complex problems.