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Optimizing the Multilayer Feed-Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 10
Year of Publication: 2014
Authors:
Osman Ahmed Abdalla
Abdelrahman Osman Elfaki
Yahya Mohammed Almurtadha
10.5120/16832-6596

Osman Ahmed Abdalla, Abdelrahman Osman Elfaki and Yahya Mohammed Almurtadha. Article: Optimizing the Multilayer Feed-Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm. International Journal of Computer Applications 96(10):42-48, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Osman Ahmed Abdalla and Abdelrahman Osman Elfaki and Yahya Mohammed Almurtadha},
	title = {Article: Optimizing the Multilayer Feed-Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {10},
	pages = {42-48},
	month = {June},
	note = {Full text available}
}

Abstract

Determination of optimum feed forward artificial neural network (ANN) design and training parameters is an extremely important mission. It is a challenging and daunting task to find an ANN design, which is effective and accurate. This paper presents a new methodology for the optimization of ANN parameters as it introduces a process of training ANN which is effective and less human-dependent. The derived ANN achieves satisfactory performance and solves the time-consuming task of training process. A Genetic Algorithm (GA) has been used to optimize training algorithms, network architecture (i. e. number of hidden layer and neurons per layer), activation functions, initial weight, learning rate, momentum rate, and number of iterations. The preliminary result of the proposed approach has indicated that the new methodology can optimize designing and training parameters precisely, and resulting in ANN where satisfactory performance.

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