A new rule generation method from neural networks formed using a genetic algorithm with virus infection
In this paper a new rule generation method from neural networks is presented. A neural network is formed using a genetic algorithm (GA) with virus infection and deterministic mutation to represent regularities in training data. This method utilizes a modular structure in GA. Each module learns a different neural network architecture, such as sigmoid and a high order neural networks. Those information is communicated to the other modules by the virus infection. The results of computer simulations show that this approach can generate obvious network structures and lead to simple rules.