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Evolutionary Intelligence

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01.03.2008 | Review Article

Neuroevolution: from architectures to learning

verfasst von: Dario Floreano, Peter Dürr, Claudio Mattiussi

Erschienen in: Evolutionary Intelligence | Ausgabe 1/2008

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Abstract

Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these problems. New insights in both neuroscience and evolutionary biology have led to the development of increasingly powerful neuroevolution techniques over the last decade. This paper gives an overview of the most prominent methods for evolving ANNs with a special focus on recent advances in the synthesis of learning architectures.

Vorheriger Artikel Genetic fuzzy systems: taxonomy, current research trends and prospects

Nächster Artikel Learning classifier systems: then and now

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Titel: Neuroevolution: from architectures to learning
verfasst von: Dario Floreano
Peter Dürr
Claudio Mattiussi
Publikationsdatum: 01.03.2008
Verlag: Springer-Verlag
Erschienen in: Evolutionary Intelligence / Ausgabe 1/2008
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-007-0002-4

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