Please use this identifier to cite or link to this item:
Title: The role of artificial neural networks in evolutionary optimisation: a review
Authors: Maarouf, M.
Sosa Marco, Adriel 
Galván-González, Blas José 
Greiner Sánchez, David Juan 
Winter Althaus, Gabriel 
Méndez Babey, Máximo Juan 
Aguasca Colomo, Ricardo 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Artificial neural networks
Evolutionary optimisation
Evolutionary algorithm
Issue Date: 2015
Publisher: Springer 
Journal: Computational Methods in Applied Sciences 
Conference: 10th EUROGEN International Conference 2013 
Abstract: This paper reviews the combination of Artificial Neural Networks (ANN) and Evolutionary Optimisation (EO) to solve challenging problems for the academia and the industry. Both methodologies has been mixed in several ways in the last decade with more or less degree of success, but most of the contributions can be classified into the two following groups: the use of EO techniques for optimizing the learning of ANN (EOANN) and the developing of ANNs to increase the efficiency of EO processes (ANNEO). The number of contributions shows that the combination of both methodologies is nowadays a mature field but some new trends and the advances in computer science permits to affirm that there is still room for noticeable improvements.
ISBN: 978-3-319-11540-5
ISSN: 1871-3033
DOI: 10.1007/978-3-319-11541-2_4
Source: Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences / David Greiner, Blas Galván, Jacques Périaux, Nicolas Gauger, Kyriakos Giannakoglou, Gabriel Winter (Eds.). Computational Methods in Applied Sciences [ISSN 1871-3033], v. 36, p. 59-76
Appears in Collections:Capítulo de libro
Show full item record


checked on Dec 3, 2023


checked on Oct 2, 2022

Page view(s)

checked on Mar 18, 2023

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.