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Joko Hariyono
Corporation & Investment Board, Yogyakarta
Computer Science & IT Control & Systems Engineering

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Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System Wahyono Wahyono; Joko Hariyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.42299

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.
Co-Authors Wahyono Wahyono