Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition

In this paper, a new model of a Modular Neural Network (MNN) optimized with hierarchical genetic algorithms is proposed. The model uses a granular approach based on the database complexity. In this case the proposed method is tested with the problem of human recognition based on the face information. The ORL and the ESSEX face databases are used to test the effectiveness of the proposed method. To compare with other related works using the same databases, four cases are established (3 for the Essex Database and 1 for the ORL Database). The results using the proposed method are better than the results achieved by other works, and this affirmation is based on a statistical comparison of results. The main idea is to design the architectures of modular neural networks using a Hierarchical Genetic Algorithm (HGA). The distribution of persons in each granule is determined by an initial analysis, resulting in a grouping of data with the same complexity. The proposed HGA allows the optimization of multiple modular neural networks that use different number of data points for the training phase, which means that in the same evolution multiple results can be obtained.