01345nas a2200253   4500000000100000000000100001008004100002260001200043653002300055653001700078653001700095653002400112653002600136100002000162700001900182700002100201700003400222245006600256856009800322300001000420490000600430520064100436022001401077       2016                            d  c09/201610aGenetic Algorithms10aOptimization10aArchitecture10aNonlinear Operation10aMultilayer Perceptron1 aHassan Ramchoun1 aYoussef Ghanou1 aMohamed Ettaouil1 aMohammed Amine Janati Idrissi00aMultilayer Perceptron: Architecture Optimization and Training  uhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2016/02/ijimai20164_1_5_pdf_30533.pdf  a26-300 v43 aThe multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.  a1989-1660