Journal
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
Volume 117, Issue -, Pages 180-191Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2017.09.006
Keywords
Deep Learning; Evolutionary Algorithms; Finite-State Machines; Automated parametrisation
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Funding
- EphemeCH [TIN2014-56494-C4-4-P]
- DeepBio [TIN2017-85727-C4-3-P]
- Spanish Ministry of Economy and Competitivity
- European Regional Development Fund FEDER
- Justice Programme of the European Union [723180]
- CAM [S2013/ICE-3095]
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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.
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