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

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

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


 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.


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

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