Interest in hybrid methods that combine artificial neural networks (ANNs) and evolutionary algorithms (EAs) has grown in the last few years, due to their robustness and ability to design networks by setting initial weight values, by searching the architecture and the learning rule and parameters. However, papers describing the way genetic operators are tested to determine their effectiveness are scarce; moreover, few researchers publish the most suitable values of these operator parameters to solve a given problem. This paper presents an exhaustive analysis of the G-Prop method, and the different parameters the method requires (population size, selection rate, initial weight range, number of training epochs, etc.) are determined. The paper also the discusses the influence of the application of genetic operators on the precision (classification ability or error) and network size in classification problems. When making a detailed statistical analysis of the influence of each parameter, the designer should pay most attention to the parameter presenting values that are statistically most significant. The significance and relative importance of the parameters with respect to the results obtained, as well as suitable values for each, were obtained using ANalysis Of the VAriance (ANOVA). Experiments show the significance of parameters concerning the neural network and learning in the hybrid methods. Combining evolutionary algorithms and neural network learning methods can lead to better results than using those methods alone. Moreover, making the network initial weights evolve is an important factor in the process. The parameters found using this method were used to compare the G-Prop method both to itself with other parameter settings, and to other published methods.
