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      Diseño Automático de Redes Neuronales Artificiales mediante el uso del Algoritmo de Evolución Diferencial (ED) Translated title: Automatic Design of Artificial Neural Networks by means of Differential Evolution (DE) Algorithm

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          Abstract

          En el área de la Inteligencia Artificial, las Redes Neuronales Artificiales (RNA) han sido aplicadas para la solución de múltiples tareas. A pesar de su declive y del resurgimiento de su desarrollo y aplicación, su diseño se ha caracterizado por un mecanismo de prueba y error, el cual puede originar un desempeño bajo. Por otro lado, los algoritmos de aprendizaje que se utilizan como el algoritmo de retropropagacion y otros basados en el gradiente descenciente, presentan una desventaja: no pueden resolver problemas no continuos ni problemas multimodales. Por esta razón surge la idea de aplicar algoritmos evolutivos para diseñar de manera automática una RNA. En esta investigación, el algoritmo de Evolución Diferencial (ED) encuentra los mejores elementos principales de una RNA: la arquitectura, los pesos sinápticos y las funciones de transferencia. Por otro lado, dos funciones de aptitud son propuestas: el error cuadraatico medio (MSE por sus siglas en inglés) y el error de clasificación (CER) las cuales, involucran la etapa de validación para garantizar un buen desempeño de la RNA. Primero se realizó un estudio de las diferentes configuraciones del algoritmo de ED, y al determinar cuál fue la mejor configuración se realizó una experimentación exhaustiva para medir el desempeño de la metodología propuesta al resolver problemas de clasificación de patrones. También, se presenta una comparativa contra dos algoritmos clásicos de entrenamiento: Gradiente descendiente y Levenberg-Marquardt.

          Translated abstract

          Artificial Neural Networks (ANN) have been applied in several tasks in the field of Artificial Intelligence. Despite their decline and then resurgence, the ANN design is currently a trial-and-error process, which can stay trapped in bad solutions. In addition, the learning algorithms used, such as back-propagation and other algorithms based in the gradient descent, present a disadvantage: they cannot be used to solve non-continuous and multimodal problems. For this reason, the application of evolutionary algorithms to automatically designing ANNs is proposed. In this research, the Differential Evolution (DE) algorithm inds the best design for the main elements of ANN: the architecture, the set of synaptic weights, and the set of transfer functions. Also two itness functions are used (the mean square error-MSE and the classification error-CER) which involve the validation stage to guarantee a good ANN performance. First, a study of the best parameter coniguration for DE algorithm is conducted. The experimental results show the performance of the proposed methodology to solve pattern classiication problems. Next, a comparison with two classic learning algorithms-gradiant descent and Levenberg-Marquardt-are presented.

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          Most cited references47

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Evolving artificial neural networks

            XIN YAO (1999)
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              Differential Evolution: A Survey of the State-of-the-Art

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                Author and article information

                Journal
                poli
                Polibits
                Polibits
                Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo (México, DF, Mexico )
                1870-9044
                December 2012
                : 46
                : 13-27
                Affiliations
                [02] México orgnameUniversidad la Salle orgdiv1Facultad de Ingeniería orgdiv2Grupo de Sistemas Inteligentes México ravem@ 123456lasallistas.oig.mx
                [01] México orgnameInstituto Politécnico Nacional orgdiv1Centro de Investigación en Computación México bgarrol@ 123456ipn.mx
                Article
                S1870-90442012000200003 S1870-9044(12)00004600003
                4e262dbe-6082-491e-98d6-24ca56341df7

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 20 July 2012
                : 22 April 2012
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 26, Pages: 15
                Product

                SciELO Mexico


                clasificación de patrones,evolución de redes neuronales artificiales,Evolución diferencial,pattern classification,evolutionary neural networks,Differential evolution

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