Abordagens evolutivas para otimização de redes neurais convolucionais baseadas em algoritmos genéticos

Abstract

Convolutional Neural Networks (CNN) are considered the state-of-the-art in computer vision applications. However, building machine learning models such method still requires the parameter and hyperparameter tuning, and considerably large training datasets. Genetic Algorithms (GA) can be a promising technique to optimize various aspects of CNNs. In this work, two approaches to optimize CNN using GA are proposed: gaCNN, for architectural optimization and MLTLGA, for transfer learning optimization. In the gaCNN, a new individual codification strategy for activation functions is proposed alongside new mutation operators. The proposed method outperformed 9 out 13 methods evaluated in classification accuracy. In the MLTLGA, a new initialization operation is presented that outperformed by at least 2% the other transfer learning methods evaluated. Therefore, the two methods are promising in the study of optimization of CNN.