Proceedings of the Annual Meeting of the Cognitive Science Society

This paper studies several applications of genetic algorithms (GAs) within the neural networks field. T he system was used to generate neural network circuit architectures. This was accomplished by using the G A to determine the weights in a fully interconnected network. The importance of the internal genetic representation was shown by testing different approaches. The effects in speed of optimization of varying the constraints imposed upon the desired network were also studied. It was observed that relatively loose constraints provided results comparable to a fully constrained system. The type of neural network circuits generated were recurrent competitive fields as described by Grossberg (1982).