Evolution of Layer Based Neural Networks: Preliminary Report
Created by W.Langdon from
gp-bibliography.bib Revision:1.7989
- @InProceedings{Pantridge:2016:GECCOcomp,
-
author = "Edward R. Pantridge and Lee Spector",
-
title = "Evolution of Layer Based Neural Networks: Preliminary
Report",
-
booktitle = "GECCO '16 Companion: Proceedings of the Companion
Publication of the 2016 Annual Conference on Genetic
and Evolutionary Computation",
-
year = "2016",
-
editor = "Tobias Friedrich and Frank Neumann and
Andrew M. Sutton and Martin Middendorf and Xiaodong Li and
Emma Hart and Mengjie Zhang and Youhei Akimoto and
Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and
Daniele Loiacono and Julian Togelius and
Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and
Faustino Gomez and Carlos M. Fonseca and
Heike Trautmann and Alberto Moraglio and William F. Punch and
Krzysztof Krawiec and Zdenek Vasicek and
Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and
Boris Naujoks and Enrique Alba and Gabriela Ochoa and
Simon Poulding and Dirk Sudholt and Timo Koetzing",
-
isbn13 = "978-1-4503-4323-7",
-
pages = "1015--1022",
-
address = "Denver, Colorado, USA",
-
month = "20-24 " # jul,
-
keywords = "genetic algorithms, genetic programming, push",
-
organisation = "SIGEVO",
-
DOI = "doi:10.1145/2908961.2931664",
-
publisher = "ACM",
-
publisher_address = "New York, NY, USA",
-
abstract = "Modern applications of Artificial Neural Networks
(ANNs)largely feature networks organized into layers of
nodes. Each layer contains an arbitrary number of
nodes, and these nodes only share edges with nodes in
certain other layers, as determined by the network's
topology. Topologies of ANNs are frequently designed by
human intuition, due to the lack of a versatile method
of determining the best topology for any given problem.
Previous attempts at creating a system to automate the
discovery of network topologies have used evolutionary
computing [6]. The evolution in these systems built
networks on a node-by-node basis, limiting the
probability of larger, layered topologies. This paper
provides on overview of Growth from Embryo of Layered
Neural Networks (GELNN), which attempts to evolve
topologies of neural networks in terms of layers, and
inter-layer connections, instead of individual nodes
and edges.",
-
notes = "Distributed at GECCO-2016.",
- }
Genetic Programming entries for
Edward R Pantridge
Lee Spector
Citations