Genetic task clustering for modular neural networks

This paper introduces a method to cluster subtasks of a complex task to be learned by neural networks. The main objectives are minimization of the epochs needed to train the clusters up to a specified error limit and to maximize the generalization rates of the trained networks. To cope with the combinatorial optimization problem involved a genetic algorithm is developed. It starts with a set of random clusters which are trained up to a stopping criterion. Based on a fitness measure derived from the training results, new clusters are assembled using genetic operators. The approach of this work is relevant for all problems being decomposable into distinct subtasks, for example in robotics and plant control, where piecewise control strategies can be learned, and in image processing. Simulations for letter recognition indicate that the method is superior to both training all tasks on large monolithic network and to training randomly assigned clusters on small modular networks.