Using a genetic algorithm to evolve an optimum input set for a predictive neural network

This paper describes an investigation into using a genetic algorithm to evolve the optimum set of inputs for a neural network. The network is to be used in a novel way for the prediction of nuclear reactor parameters under fault conditions. The development of transients is calculated in a recursive manner. The previous work and the next stage of research are described. The procedure and genetic algorithm options, including fitness, are discussed along with explanations. Finally an outline of the remaining work is introduced