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Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 1 (2019) · pp. 63–72

DOI
10.22146/ijccs.42299
ISSN
2460-7258, 1978-1520
Published
2019-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.

Keywords

Collaboration network of the authors

15 authors · 28 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • University of Ulsan
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Dzulfikar FauziUniversitas Gadjah Mada2
Chasandra PuspitasariUniversitas Gadjah Mada1
Kasliono KaslionoUniversitas Gadjah Mada1
Wahyu Sri MulyaniUniversitas Gadjah Mada1
Laksono KurnianggoroUniversity of Ulsan1
Agus HarjokoUniversitas Gadjah Mada1
Abdul KholikUniversitas Gadjah Mada1
Suprapto SupraptoUniversitas Gadjah Mada1
Yaqutina Marjani SantosaUniversitas Gadjah Mada1
Agfianto Eko PutraUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Riska Amalia PraptiwiUniversitas Gadjah Mada1
Aliyyah Nur AzhariUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System</dc:title>
	<dc:creator>Wahyono, Wahyono</dc:creator>
	<dc:creator>Hariyono, Joko</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">convolutional neural network (CNN); CNN architecture; evolutionary computing; genetic algorithm; classification system; vehicle type classification</dc:subject>
	<dc:description xml:lang="en-US"> Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada</dc:contributor>
	<dc:date>2019-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/42299</dc:identifier>
	<dc:identifier>10.22146/ijccs.42299</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 1 (2019): January; 63-72</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/42299/23720</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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