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

Wahyono Wahyono (Department of Computer Science and Electronics, Universitas Gadjah Mada)
Joko Hariyono (Corporation & Investment Board, Yogyakarta)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Vol 13, No 1 (2019): January

 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.