Evolving a neural network

Although artificial neural networks have been shown to be effective in the computation of solutions to difficult problems a general theory has not yet been developed to provide guidance in their design and implementation. Genetic algorithms have also been shown to be effective in evolving solutions to optimization problems which involve objective functions that are not `nice´. The approach presented here is the utilization of genetic algorithms to evolve the number of neurons in an artificial neural network, the weights of their interconnects, and the interconnect structure itself. With this approach, no a priori assumptions about interconnect structure, weights, number of layers. or to which neurons the inputs or outputs are connected need to be made. A combined neural network evaluation and genetic algorithm evaluation program has been written in C on a Sun workstation. The method has been successfully applied to the 9×9 bit character recognition problem