GENERAL ASYMMETRIC NEURAL NETWORKS AND STRUCTURE DESIGN BY GENETIC ALGORITHMS

A learning algorithm for neural networks based on genetic algorithms is proposed. The concept leads in a natural way to a model for the explanation of inherited behavior. Explicitly we study a simplified model for a brain with sensory and motor neurons. We use a general asymmetric network whose structure is solely determined by an evolutionary process. This system is simulated numerically. It turns out that the network obtained by the algorithm reaches a stable state after a small number of sweeps. Some results illustrating the learning capabilities are presented.