Skip to Main content Skip to Navigation
New interface
Journal articles

Artificial Neuron–Glia Networks Learning Approach Based on Cooperative Coevolution

Abstract : Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However , the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this article, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on 5 classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.
Complete list of metadata

Cited literature [72 references]  Display  Hide  Download
Contributor : Pablo Mesejo Santiago Connect in order to contact the contributor
Submitted on : Wednesday, October 28, 2015 - 11:47:15 AM
Last modification on : Thursday, October 6, 2022 - 4:04:06 PM
Long-term archiving on: : Friday, January 29, 2016 - 1:10:07 PM


Files produced by the author(s)



Pablo Mesejo, Óscar Ibáñez, Enrique Fernández-Blanco, Francisco Cedrón, Alejandro Pazos, et al.. Artificial Neuron–Glia Networks Learning Approach Based on Cooperative Coevolution. International Journal of Neural Systems, 2015, 25 (4), ⟨10.1142/S0129065715500124⟩. ⟨hal-01221226⟩



Record views


Files downloads