The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Mingo López, Fernando de and Gómez Blas, Nuria and Arteta Albert, Alberto (2012). The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.. "Journal of Computational Science", v. 1 (n. 2); pp. 46-55. ISSN 1877-7503. https://doi.org/10.1016/j.jocs.2011.12.005.

Description

Title: The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.
Author/s:
  • Mingo López, Fernando de
  • Gómez Blas, Nuria
  • Arteta Albert, Alberto
Item Type: Article
Título de Revista/Publicación: Journal of Computational Science
Date: 2012
ISSN: 1877-7503
Volume: 1
Subjects:
Faculty: E.U. de Informática (UPM)
Department: Organización y Estructura de la Información [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

More information

Item ID: 15816
DC Identifier: http://oa.upm.es/15816/
OAI Identifier: oai:oa.upm.es:15816
DOI: 10.1016/j.jocs.2011.12.005
Official URL: http://www.journals.elsevier.com/journal-of-computational-science/
Deposited by: Memoria Investigacion
Deposited on: 07 Nov 2013 09:53
Last Modified: 21 Apr 2016 16:07
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