The Evolution of Artificial Neurogenesis

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@InProceedings{2931671,

• author = "Dennis Wilson and Sylvain Cussat-Blanc and Herve Luga",
• title = "The Evolution of Artificial Neurogenesis",
• booktitle = "GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion",
• year = "2016",
• isbn13 = "978-1-4503-4323-7",
• pages = "1047--1048",
• address = "Denver, Colorado, USA",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4503-4323-7",
• DOI = "doi: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.",

}

Genetic Programming entries for Dennis G Wilson Sylvain Cussat-Blanc Herve Luga

Citations