Hierarchical genetic optimization of modular granular neural networks for ear recognition

In this paper a new model of a Modular Neural Network (MNN) with a granular approach is proposed, also a Hierarchical Genetic Algorithm (HGA) is proposed, with the goal of obtaining an optimal number of sub modules and optimal percentage of data for training. The model was applied to pattern recognition based on the ear biometrics. The proposed method is able to divide the data automatically into sub modules, to work with a percentage of images and select which are the optimal images to be used for training.