|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
|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.||URI:||http://hdl.handle.net/10553/54813||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|
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