Learning and structuring of neural networks using genetic algorithm and linear programming

Proposes a method for generating neural networks using genetic algorithms and linear programming; the genetic algorithm is used to decide the structure of neural net, while the linear programming is used for learning. This method holds a feature that there is no parameter sensitive to the speed and the precision of learning unlike the usual backpropagation. The effectiveness of the method is shown on the basis of several examples.