MONSTER — The Ghost in the Connection Machine: Modularity of Neural Systems in Theoretical Evolutionary Research

Abstract

Both genetic algorithms (GAs) and artificial neural networks (ANNs) (connectionist learning models) are effective generalisations of successful biological techniques to the artificial realm. Both techniques are inherently parallel and seem ideal for implementation on the current generation of parallel supercomputers. We consider how the two techniques complement each other and how combining them (i.e. evolving artificial neural networks with a genetic algorithm), may give insights into the evolution of structure and modularity in biological brains. The incorporation of evolutionary and modularity concepts into artificial systems has the potential to decrease the development time of ANNs for specific image and information processing applications. General considerations when genetically encoding ANNs are discussed, and a new encoding method developed, which has the potential to simplify the generation of complex modular networks. The implementation of this technique on a CM-5 parallel supercomputer raises many practical and theoretical questions in the application and use of evolutionary models with artificial neural networks.