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MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models by Özkan İni̇k; Mustafa Altıok; Erkan Ülker; Barış Koçer is a Computer Science article available to read on EtoBox.

What is MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models about?

Convolutional neural networks (CNNs) have been used to solve many problems in computer science with a high level of success, and have been applied in many fields in recent years. However, most of the designs of these models are still tuned manually; obtaining the highest performing CNN model is therefore very time-consuming, and is sometimes not achievable. Recently, researchers have started using optimization algorithms for the automatic adjustment of the hyper-parameters of CNNs. In particular, single-objective optimization algorithms have been used to achieve the highest network accuracy for the design of a CNN. When these studies are examined, it can be seen that the most significant problem in the optimization of the parameters of a CNN is that a great deal of time is required for tuning. Hence, optimization algorithms with high convergence rates are needed for the parameter optimization of deep networks. In this study, we first develop an algorithm called MODE-CNN, based on the multi-objective differential evolution (MODE) algorithm for parameter optimization of CNN or CNN-based methods. MODE-CNN is then compared with four different multiobjective optimization algorithms. Thi

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Author

Özkan İni̇k; Mustafa Altıok; Erkan Ülker; Barış Koçer

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)