Apply genetic algorithm to the learning phase of a neural network

Natural networks have been used during several years to solve classification problems. The performance of a neural network depends directly on the design of the hidden layers, and in the calculation of the weights that connect the different nodes. On this project, the structure of the hidden layer is not modified, as the interest lies only on the calculation of the weights of the system. In order to obtain a feasible result, the weights of the neural network are calculated due a function cost. A genetic algorithm approach is presented and compared with the gradient descent in failure rate and time to obtain a solution.