Evolution of Layer Based Neural Networks: Preliminary Report

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@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

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