Evolutionary modular neural networks for intelligent systems

The evolutionary approach to artificial neural networks has been rapidly developing in recent years and shows great potential as a powerful tool. However, most evolutionary neural networks have paid little attention to the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and evolutionary algorithm as a promising model for intelligent systems. To build a neural network system that is rich in autonomy and creativity, some ideas of artificial life have been adopted. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which might not only develop spontaneously new functionality, but also grow and evolve its own structure autonomously. We show the potential of the method by applying it to a visual categorization task with handwritten digits. The evolutionary mechanism has shown a strong potential to generate useful network architectures from an initial set of randomly connected networks.