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dc.contributor.author | Martín, Alejandro![]() |
es_ES |
dc.contributor.author | Lara-Cabrera, Raúl![]() |
es_ES |
dc.contributor.author | Fuentes-Hurtado, Félix José![]() |
es_ES |
dc.contributor.author | Naranjo Ornedo, Valeriana![]() |
es_ES |
dc.contributor.author | Camacho, David![]() |
es_ES |
dc.date.accessioned | 2020-06-12T03:32:44Z | |
dc.date.available | 2020-06-12T03:32:44Z | |
dc.date.issued | 2018-07 | es_ES |
dc.identifier.issn | 0743-7315 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/146154 | |
dc.description.abstract | [EN] 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. | es_ES |
dc.description.sponsorship | This work has been co-funded by the next research projects: EphemeCH (TIN2014-56494-C4-4-P) and DeepBio (TIN2017-85727-C4-3-P) Spanish Ministry of Economy and Competitivity and European Regional Development Fund FEDER, Justice Programme of the European Union (2014-2020) 723180 -RiskTrack-JUST-2015-JCOO-AG/JUST-2015-JCOO-AG-1, and by the CAM grant S2013/ICE-3095 (CIBERDINE:Cybersecurity, Data and Risks). The contents of this publication are the sole responsibility of their authors and can in no way be taken to reflect the views of the European Commission. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Journal of Parallel and Distributed Computing | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Evolutionary algorithms | es_ES |
dc.subject | Finite-State machines | es_ES |
dc.subject | Automated parametrisation | es_ES |
dc.subject.classification | TEORIA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.jpdc.2017.09.006 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2014-56494-C4-4-P/ES/ALGORITMOS BIOINSPIRADOS EN ENTORNOS EFIMEROS COMPLEJOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/723180/EU//RiskTrack/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85727-C4-3-P/ES/NUEVOS MODELOS DE COMPUTO BIOINSPIRADO PARA ENTORNOS MASIVAMENTE COMPLEJOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CAM//S2013%2FICE-3095/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions | es_ES |
dc.description.bibliographicCitation | Martín, A.; Lara-Cabrera, R.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Camacho, D. (2018). EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation. Journal of Parallel and Distributed Computing. 117:180-191. https://doi.org/10.1016/j.jpdc.2017.09.006 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.jpdc.2017.09.006 | es_ES |
dc.description.upvformatpinicio | 180 | es_ES |
dc.description.upvformatpfin | 191 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 117 | es_ES |
dc.relation.pasarela | S\350444 | es_ES |
dc.contributor.funder | Comunidad de Madrid | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |