title: 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 paper, 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 five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.
authors.affiliation: Department of Information Engineering, University of Parma, Parma 43124, Italy , ISIT-UMR 6284 CNRS, University of Auvergne, Clermont-Ferrand 63000, France.
authors.forename: Pablo
authors.initials: P
authors.lastname: Mesejo
authors.forename: Oscar
authors.initials: O
authors.lastname: Ibáñez
authors.forename: Enrique
authors.initials: E
authors.lastname: Fernández-Blanco
authors.forename: Francisco
authors.initials: F
authors.lastname: Cedrón
authors.forename: Alejandro
authors.initials: A
authors.lastname: Pazos
authors.forename: Ana B
authors.initials: AB
authors.lastname: Porto-Pazos
mesh_terms: D000465:Algorithms; D005823:Genetics; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009457:Neuroglia / Q000502:physiology*; D009474:Neurons / Q000502:physiology*
keywords: Artificial neuron–glia networks; artificial astrocytes; artificial neural networks; classification; cooperative coevolutionary genetic algorithm; evolutionary algorithms; genetic algorithms; glial cells; parameter optimization