Modular granular neural networks optimization with Multi-Objective Hierarchical Genetic Algorithm for human recognition based on iris biometric
Daniela Sánchez, Patricia Melín, Oscar Castillo, Fevrier Valdez · 2013
In this paper a new model of a Multi-Objective Hierarchical Genetic Algorithm (MOHGA) based on the Micro Genetic Algorithm (μGA) approach for Modular Neural Networks (MNNs) optimization is proposed. The proposed method can divide the data automatically into granules or sub modules, and chooses which data are for the training and which are for the testing phase. The proposed Multi-Objective Genetic Algorithm is responsible for determining the number of granules or sub modules and the percentage of data for training that can allow to have better results. The proposed method was applied to human recognition and its applicability with good results is shown, although the proposed method can be used in other applications such as time series prediction and classification.