Evolving neural networks: A comparison between differential evolution and particle swarm optimization

Beatriz A. Garro, Humberto Sossa, Roberto A. Vázquez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

16 Citas (Scopus)

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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings
Páginas447-454
Número de páginas8
EdiciónPART 1
DOI
EstadoPublicada - 2011
Evento2nd International Conference on Swarm Intelligence, ICSI 2011 - Chongqing, China
Duración: 12 jun. 201115 jun. 2011

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NúmeroPART 1
Volumen6728 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia2nd International Conference on Swarm Intelligence, ICSI 2011
País/TerritorioChina
CiudadChongqing
Período12/06/1115/06/11

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