Home > Archive > 2019 > Volume 9 Number 6 (Dec. 2019) >
IJMLC 2019 Vol.9(6): 788-794 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.6.874

Optimal Convolutional Neural Network Architecture Design Using Clonal Selection Algorithm

Ali Al Bataineh and Devinder Kaur

Abstract—In this paper, the Clonal Selection Algorithm (CSA) is implemented to determine the optimal architecture of a convolutional neural network (CNN) for the classification of handwritten character digits. The efficacy of CNN in image recognition is one of the central motives why the world has woken up to the effectiveness of deep learning. During training, an optimal CNN architecture can extract complex features from the data that is being trained; however, the ideal architecture of a CNN for a specific problem cannot be determined by some standard procedure. In practice, CNN architectures are generally designed using human expertise and domain knowledge. By using CSA, optimal architecture of CNN can be determined autonomously through evolution of hyperparameters of the architecture for a given dataset. In this work, proposed methodology is tested on EMNIST dataset which is an enhanced version of MNIST dataset. The results have proven that the CSA based tuning is capable of generating optimal CNN architectures. Through this proposed technique, the best architecture of CNN for a given problem can be self-determined without any human intervention.

Index Terms—Convolutional neural network, clonal selection algorithm, EMNIST dataset.

The authors are with the EECS Department, the University of Toledo, Toledo, OH 43606 USA (e-mail: ali.albataineh@utoledo.edu, devinder.kaur@utoledo.edu).


Cite: Ali Al Bataineh and Devinder Kaur, "Optimal Convolutional Neural Network Architecture Design Using Clonal Selection Algorithm," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 788-794, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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