EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation

Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has been widely used to solve problems of varied complexity, due to their ability to make models fitted to non-linear complex problems. Despite its well-known benefits, DNNs are complex learning models whose parametrisation and architecture are made usually by hand. This paper proposes a new Evolutionary Algorithm, named EvoDeep. devoted to evolve the parameters and the architecture of a DNN in order to maximise its classification accuracy, as well as maintaining a valid sequence of layers. This model is tested against a widely used dataset of handwritten digits images. The experiments performed using this dataset show that the Evolutionary Algorithm is able to select the parameters and the DNN architecture appropriately, achieving a 98.93% accuracy in the best run. (C) 2017 Elsevier Inc. All rights reserved.