Learning Artificial Neural Networks multiclassifiers by evolutionary multiobjective differential evolution guided by statistical distributions

This work presents an Evolutionary Artificial Neural Network (EANN) approach based on the Pareto Differential Evolution (PDE) algorithm where the crossover operator is determined using a Gaussian distribution associated with the best models in the evolutionary population. The crossover operator used in a real-coded genetic algorithm is based on confidence intervals. The PDE is used to localize the most promising search regions for locating the best individuals. Confidence intervals use mean localization and standard deviation estimators that are highly recommendable when the distribution of the random variables is Gaussian. It has always been an issue to find good ANN architecture in both multiclassification problems and in the field of ANNs. EANNs provide a better method to optimize simultaneously both network performance (based on the Correct Classification Rate, C) and the network performance of each class (Minimum Sensitivity, MS). The proposal with respect to methodology performance is evaluated using a well characterized set of multiclassification benchmark problems. The results show that crossover performance based on confidence intervals is less dependent on the problem than crossover performance based on a random selection of three parents in the PDE.