Gen2Gen: Efficiently Training Artificial Neural Networks Using a Series of Genetic Algorithms

Artificial neural networks have been used in a multitude of applications in various research areas in recent decades, providing excellent results in both data classification and data fitting. Their success is based on the effective identification (training) of their parameters using optimization techniques, and hence a series of programming methods have been developed for training these models. However, many times these techniques either can identity only some local minima of the error function with poor overall results or present overfitting problems in which the performance of the artificial neural network is significantly reduced when it is applied to different data from the training set. This manuscript introduces a method for the efficient training of artificial neural networks, where a series of genetic algorithms is applied to the network parameters in several stages. In the first stage, an initial identification of the network value interval is performed; in the second stage, the initial estimate of the value interval is improved; and in the third stage, the final adjustment of the network parameters within the previously identified value interval takes place. The new method was tested on some classification and regression problems found in the relevant literature, and the experimental results were compared against the results obtained by the application of other well-known methods used for neural network training.