| Título |
Evolving Neural Networks: A Comparison between Differential Evolution and Particle Swarm Optimization |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 2nd International Conference on Swarm Intelligence |
| Resumen |
Due to their efficiency and adaptability, bio-inspired algorithms have shown their usefulness in a wide range of different non-linear optimization problems. In this paper, we compare two ways of training an artificial neural network (ANN): Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The main contribution of this paper is to show which of these two algorithms provides the best accuracy during the learning phase of an ANN. First of all, we explain how the ANN training phase could be seen as an optimization problem. Then, we explain how PSO and DE could be applied to find the best synaptic weights of the ANN. Finally, we perform a comparison between PSO and DE approaches when used to train an ANN applied to different non-linear problems. |
| Observaciones |
Advances in Swarm Intelligence, ICSI 2011; (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Code 85128 |
| Lugar |
Chongqing |
| País |
China |
| No. de páginas |
447-454 |
| Vol. / Cap. |
6728 |
| Inicio |
2011-06-12 |
| Fin |
2011-06-15 |
| ISBN/ISSN |
978-3-642-21514-8 |
