Evolutionary Convolutional Neural Networks Using ABC

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.