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The Evolution of Artificial Neurogenesis

Wilson, Dennis and Cussat-Blanc, Sylvain and Luga, Hervé The Evolution of Artificial Neurogenesis. (2016) In: Genetic and Evolutionary Computation Conference Companion (GECCO 2016), 20 July 2016 - 24 July 2016 (Denver, United States).

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Official URL: http://dx.doi.org/10.1145/2908961.2931671

Abstract

Evolutionary development as a strategy for the design of artificial neural networks is an enticing idea, with possible inspiration from both biology and existing indirect representations. A growing neural network can not only optimize towards a specific goal, but can also exhibit plasticity and regeneration. Furthermore, a generative system trained in the optimization of the resultant neural network in a reinforcement learning environment has the capability of on-line learning after evolution in any reward-driven environment. In this abstract, we outline the motivation for and design of a generative system for artificial neural network design.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to ACM. The definitive version is available at http://dl.acm.org This papers appears in GECCO'16 ISBN: 978-1-4503-4323-7 The original PDF is available at: https://dl.acm.org/citation.cfm?doid=2908961.2931671
HAL Id:hal-01782552
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:27 Mar 2018 12:53

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