A Method to Generate Universal Rules by Using a Genetic Algorithm with Virus Infection

Abstract

In this paper a method to generate universal rukes by using a genetic algorithm with virus infection is presented. This method utilizes a modular structure in GA. Each module learns a different neural network architecture, such as a sigmoid and a higher order (GMDH-type) neural networks. Those information is communicated to the other modules by the virus infection. This GA can generate different kinds of rules for different problems by using the same algorithm. The results of computer simulations show that this approach can generate obvious network structures and as a result simple rules for a pattern classification and a chaotic identification problems.