<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, genetic algorithm (GA) is used to optimally determine the architecture of a convolutional neural network (CNN) that is used to classify handwritten numbers. The CNN is a class of deep feed-forward network, which have seen major success in the field of visual image analysis. During training, a good CNN architecture is capable of extracting complex features from the given training data; however, at present, there is no standard way to determine the architecture of a CNN. Domain knowledge and human expertise are required in order to design a CNN architecture. Typically architectures, The GA determine the exact architecture of a CNN by evolving the various hyper parameters of the architecture for a given application. The proposed method was tested on the MNIST dataset. The results show that the genetic algorithm is capable of generating successful CNN architectures. The proposed method performs the entire process of architecture generation without any human intervention.</p>
</abstract>

In this paper, genetic algorithm (GA) is used to optimally determine the architecture of a convolutional neural network (CNN) that is used to classify handwritten numbers. The CNN is a class of deep feed-forward network, which have seen major success in the field of visual image analysis. During training, a good CNN architecture is capable of extracting complex features from the given training data; however, at present, there is no standard way to determine the architecture of a CNN. Domain knowledge and human expertise are required in order to design a CNN architecture. Typically architectures, The GA determine the exact architecture of a CNN by evolving the various hyper parameters of the architecture for a given application. The proposed method was tested on the MNIST dataset. The results show that the genetic algorithm is capable of generating successful CNN architectures. The proposed method performs the entire process of architecture generation without any human intervention.

Designing Convolutional Neural Network Architecture Using Genetic Algorithms

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Designing Convolutional Neural Network Architecture Using Genetic Algorithms"}

In this paper, genetic algorithm (GA) is used to optimally determine the architecture of a convolutional neural network (CNN) that is used to classify...

Designing Convolutional Neural Network Architecture Using Genetic Algorithms

Online veröffentlicht: 22. Feb. 2021

Seitenbereich: 26 - 35

DOI: https://doi.org/10.21307/ijanmc-2021-024

Schlüsselwörter
Convolutional Neural Network, Genetic Algorithm, MNIST Data

© 2021 Ashray Bhandare et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Designing Convolutional Neural Network Architecture Using Genetic Algorithms

Online veröffentlicht: 22. Feb. 2021

Seitenbereich: 26 - 35

DOI: https://doi.org/10.21307/ijanmc-2021-024

SchlüsselwörterConvolutional Neural Network, Genetic Algorithm, MNIST Data

© 2021 Ashray Bhandare et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Convolutional Neural Network, Genetic Algorithm, MNIST Data