Process control using genetically trained neural networks

Abstract This paper presents a novel approach to the general problem of the control of processes whose dynamic characteristics are not known, or little known. It demonstrates how a system consisting of a relatively small number of neuronlike elements can be used to control a wide variety of processes with little or no prior knowledge of the process to be controlled. The general procedure involved uses a form of simulated evolution in which a group of controllers, each of which is represented by a small neural network, gradually improves over time by combining their connection weights or by small mutational changes to their weights, together with selective reproduction using fitness values assigned to each network based on a global evaluation of each network's performance.