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

https://doi.org/10.22146/ijccs.42299

Wahyono Wahyono(1*), Joko Hariyono(2)

(1) Department of Computer Science and Electronics, Universitas Gadjah Mada
(2) Corporation & Investment Board, Yogyakarta
(*) Corresponding Author

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


convolutional neural network (CNN); CNN architecture; evolutionary computing; genetic algorithm; classification system; vehicle type classification

Full Text:

PDF


References

[1] E. P. I. W. Suartika, A. Y. Wijaya, and Soelaiman, R., “Image Classification Using Convolutional Neural Network (CNN) on Caltech 101”, Jurnal Teknik ITS Vol. 5, No. 1, 2016. ISSN: 2337-3539 (2301-9271 Print)

[2] E. S. Marquez, J. S. Hare, and M. Niranjan, “Deep Cascade Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol.29, no. 11, pp. 5475-5485, November 2018. Available: https://ieeexplore.ieee.org/document/8307262.

[3] W. Rawat and Z. Wang, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” Neural Computation, vol.29, no.9, pp. 2352-2449, September 2017. Available: https://ieeexplore.ieee.org/document/8016501.

[4] M. Zufar and B. Setiyono, “Convolutional Neural Networks for Real-Time Face Recognition”, Jurnal Sains dan Seni ITS Vol. 5 No. 2, pp.2337-3520 (2301-928X Print), 2016.

[5] J. He and G. Lin, “Average Convergence Rate of Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation, vol.20, no.2, pp.316-321, 2016. Available: https://ieeexplore.ieee.org/document/7122298.

[6] D. Corus, D.-C. Dang, A. V. Eremeev, and P. K. Lehre, “Level-Based Analysis of Genetic Algorithms and Other Search Processes”. IEEE Transactions on Evolutionary Computation, vol.22, no.5, pp.707-719, 2018. Available: https://ieeexplore.ieee.org/document/8039236.

[7] Y.-J. Gong, J.-J. Li, Y. Zhou, Y. Li, H. S.-H. Chung, Y.-H. Shi, and J. Zhang, “Genetic Learning Particle Swarm Optimization”, IEEE Transactions on Cybernetics, vol.46, no.10, pp. 2277-2290, 2016. Available: https://ieeexplore.ieee.org/document/7271066.

[8] I. Candradewi, “Video processing for vehicle classification based on support vector machine”, Master thesis, Graduate School of Computer Science, Universitas Gadjah Mada, Yogyakarta, 2015.

[9] B. Pribadi and M. Naseer, “Vehicle Type Classification System Through Digital Image Technique”, SETRUM – Volume 3, No. 2, December 2014

[10] M. Irfan, “Digital Image-Based Vehicle Classification System with the Multilayer Perceptron Method”, IJEIS (Indonesian J. Electron. Instrum. Syst.), Vol.7, No.2, October 2017, pp. 139-148

[11] A. K. Wardana and S. Hartati, “Scheduling System of Pencak Silat Based on Genetic Algorithm,” IJCCS (Indonesian J. Comput. Cybern. Syst.), vol. 11, no. 2, p. 177, July 2017 [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/24214. [Accessed: 24-September-2018]

[12] Wahyono, C. Puspitasari, M. D. Fauzi, K. Kasliono, W. S. Mulyani, and L. Kurnianggoro, “An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm” IJCCS (Indonesian J. Comput. Cybern. Syst.), vol. 12, no. 2, July 2018 [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/36154. [Accessed: 24-December-2018]

[13] K. Hakiim, A. Darmawan, and Faizah, “Optimization of PID Control using Genetic Algorithms for Quadrotor Flights,” IJEIS (Indonesian J. Electron. Instrum. Syst.), vol. 7, no. 2, October 2017 [Online]. Available: https://jurnal.ugm.ac.id/ijeis/article/view/2321. [Accessed: 29-September-2018]



DOI: https://doi.org/10.22146/ijccs.42299

Article Metrics

Abstract views : 4157 | views : 3689

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs


View My Stats1
View My Stats2