Name
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 utilized 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 ofneural networks in terms of layers, and inter-layer connections, instead of individual nodes and edges. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2908961.2931664 | GECCO (Companion) |
Field | DocType | Citations |
Evolutionary acquisition of neural topologies,Computer science,Evolutionary computation,Network topology,Neuroevolution of augmenting topologies,Genetic programming,Hierarchical network model,Artificial intelligence,Artificial neural network,Neuroevolution,Machine learning,Distributed computing | Conference | 1 |
PageRank | References | Authors |
0.38 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edward R. Pantridge | 1 | 1 | 0.38 |
| Lee Spector | 2 | 195 | 17.32 |