Layers Sequence Optimizing for Deep Neural Networks using Multiples Objectives

Selecting the best architecture for a Deep Neural Network (DNN) is a non-trivial task since there is a massive amount of possible configurations (layers and parameters) and great difficulty in how to choose them. To make this task more independent of human interaction, this work addresses the DNN architecture selection problem as a multi-objective optimization task with different criteria in a combinatorial context. For this, we defined a new way to represent the architecture of DNN (layer sequence) as a solution in the optimization process. The proposed method attempts to find the best composition and sequence of layers for the DNN architecture satisfying two criteria: accuracy and $F_{1} {score}$. The method was evaluated for performance and compared to the exhaustive and random approaches and state-of-the-art DNN algorithms. The results obtained showed that the proposed method is capable of achieving results close to the optimum, and competitive when compared to those results reached by state of the art algorithms.