@inproceedings{bd44a8dd5a944bda8d57c1a8fa6e1417,
title = "Design of artificial neural networks using differential evolution algorithm",
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 non-linear 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.",
author = "Garro, {Beatriz A.} and Humberto Sossa and V{\'a}zquez, {Roberto A.}",
note = "Funding Information: Acknowledgements. Authors thank SIP-IPN under grant 20100468 and COFAA for the economical support. They also thank the European Union, the European Commission and CONACYT for the economical support. This paper has been prepared by economical support of the European Commission under grant FONCICYT 93829. The content of this paper is an exclusive responsibility of the CIC-IPN and it cannot be considered that it reflects the position of the European Union. We thank also the reviewers for their comments for the improvement of this paper. Funding Information: Authors thank SIP-IPN under grant 20100468 and COFAA for the economical support. They also thank the European Union, the European Commission and CONACYT for the economical support. This paper has been prepared by economical support of the European Commission under grant FONCICYT93829. The content of this paper is an exclusive responsibility of the CIC-IPN and it cannot be considered that it reflects the position of the European Union. We thank also the reviewers for their comments for the improvement of this paper.",
year = "2010",
doi = "10.1007/978-3-642-17534-3_25",
language = "Ingl{\'e}s",
isbn = "3642175333",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "201--208",
booktitle = "Neural Information Processing",
address = "Alemania",
edition = "PART 2",
}