Design of artificial neural networks using differential evolution algorithm

Authors:
Beatriz A. Garro;Humberto Sossa;Roberto A. Vázquez
Affiliations:
Centro de Investigación en Computación, IPN, Ciudad de México, México;Centro de Investigación en Computación, IPN, Ciudad de México, México;Escuela de Ingeniería, Universidad La Salle, México, D.F
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

The design of an Artificial Neural Network (ANN) is a difficult task for it depends on the human experience. Moreover it needs a process of testing and error to select which kind of a transfer function and which algorithm should be used to adjusting the synaptic weights in order to solve a specific problem. In the last years, bio-inspired algorithms have shown their power in different nonlinear optimization problems. Due to their efficiency and adaptability, in this paper we explore a new methodology to automatically design an ANN based on the Differential Evolution (DE) algorithm. The proposed method is capable to find the topology, the synaptic weights and the transfer functions to solve a given pattern classification problems.