The Evolution of Artificial Neurogenesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @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