The Evolution of Artificial Neurogenesis

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  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
  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

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