@inproceedings{publications29194,
       booktitle = {Genetic and Evolutionary Computation Conference Companion (GECCO 2016)},
           title = {The Evolution of Artificial Neurogenesis},
          author = {Dennis Wilson and Sylvain Cussat-Blanc and Herv{\'e} Luga},
       publisher = {ACM},
            year = {2016},
           pages = {1047--1048},
        keywords = {Artificial intelligence - Cognitive science - Neural networks},
             url = {https://publications.ut-capitole.fr/id/eprint/29194/},
        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.}
}