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Descripción
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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 parametrisationand 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. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Journal of Parallel And Distributed Computing |
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ISSN
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0743-7315 |
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Factor de impacto JCR
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1,93 |
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Información de impacto
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Datos JCR del año 2016 |
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Volumen
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117 |
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DOI
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10.1016/j.jpdc.2017.09.006 |
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Número de revista
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Desde la página
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180 |
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Hasta la página
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191 |
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Mes
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SIN MES |
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Ranking
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34/104 COMPUTER SCIENCE, THEORY & METHODS |