Abstract
Convolutional neural networks (CNNs) have been used over the past years to solve many different artificial intelligence (AI) problems, providing significant advances in some domains and leading to state-of-the-art results. Nonetheless, the design of CNNs architecture remains to be a meticulous and cumbersome process that requires the participation of specialists in the field. In this work, we have explored the neuro-evolution application to the automatic design of CNN topologies, developing a novel solution based on Artificial Bee Colony (ABC). The MNIST dataset is used to evaluate the proposed method, which is proved being highly competitive with the state-of-the-art.