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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings
Pages447-454
Number of pages8
EditionPART 1
DOIs
StatePublished - 2011
Event2nd International Conference on Swarm Intelligence, ICSI 2011 - Chongqing, China
Duration: 12 Jun 201115 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6728 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Swarm Intelligence, ICSI 2011
Country/TerritoryChina
CityChongqing
Period12/06/1115/06/11

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