Вісник ХНУ. Технічні науки - 2020 рік

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Application of a genetic algorithm to search for the optimal convolutional neural network architecture with weight distribution
(Хмельницький національний університет, 2020) Radiuk, P.M.; Радюк, П.М.
In the past decade, a new way in neural networks research called Network architectures search has demonstrated noticeable results in the design of architectures for image segmentation and classification. Despite the considerable success of the architecture search in image segmentation and classification, it is still an unresolved and urgent problem. Moreover, the neural architecture search is also a highly computationally expensive task. This work proposes a new approach based on a genetic algorithm to search for the optimal convolutional neural network architecture. We integrated a genetic algorithm with standard stochastic gradient descent that implements weight distribution across all architecture solutions. This approach utilises a genetic algorithm to design a sub-graph of a convolution cell, which maximises the accuracy on the validation set. We show the performance of our approach on the CIFAR-10 and CIFAR-100 datasets with a final accuracy of 93.21% and 78.89%, respectively. The main scientific contribution of our work is the combination of genetic algorithm with weight distribution in the architecture search tasks that achieve similar to state-of-the-art results on a single GPU.