Evolutionary single hidden-layer feed forward networks. (23rd December 2014)

• Record Type: Journal Article
• Authors: Safi, Youssef; Bouroumi, Abdelaziz
• Is Part Of: International journal of innovative computing and applications. Volume 6:Number 2(2014)
• Page Start: 73
• Page End: 86
• Publication Date: 2014-12-23
• Subjects: artificial neural networks -- ANNs -- evolutionary algorithms -- classification -- prediction -- machine learning -- evolutionary artificial neural networks -- feed forward neural networks -- evolutionary strategies -- Proben1 -- supervised learning -- multilayer perceptron -- MLP -- artificial neural networks optimisation; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

We propose an evolutionary method for optimising both the architecture and the synaptic weights of single hidden-layer feed forward neural networks. Based on evolutionary strategies, this method uses new genetic operators of mutation and recombination in order to evolve a population of candidate solutions in the form of neural networks with different architectures. Experimental results are presented to demonstrate the effectiveness of the proposed method in both classification and prediction problems. These results concern six well-known benchmark problems and are compared to those produced for the same problems by three other methods.