Ghanou, Y. and Bencheikh, G. (2016) Architecture optimization and training for the multilayer perceptron using ant system. IAENG International Journal of Computer Science, 43 (1). pp. 20-26.

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Abstract

we present in this paper an Ant Colony Algorithm to optimize the performance of the multilayer Perceptron. Indeed, the performance of the multilayer Perception depends on its parameters such as the number of neurons in the hidden layer and the connection weights. In this respect, we firstly model the problem of neural architecture and training in terms of a mixed-integer problem with a linear constraint, and secondly, we propose an Ant Colony Algorithm to solve it. The experimental results illustrate the advantage of our approach as a new method of training and architecture optimization.

Item Type: Article
Uncontrolled Keywords: Algorithms; Ant colony optimization; Classification (of information); Multilayers; Neural networks; Nonlinear programming, Ant colony algorithms; Multi layer perceptron; Neural architectures; Non-linear optimization; Supervised trainings, Optimization
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2605

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