Pavlo Radiuk

Khmelnytskyi National University

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Olexander Barmak

Khmelnytskyi National University

ЮК

Юрий Крак

Taras Shevchenko National University of Kyiv

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Олег Савенко

Khmelnytskyi National University

Eduard Manziuk

Khmelnytskyi National University

Serhii Svystun

Anatoliy Sachenko

West Ukrainian National University

Andrii Lysyi

Oleksandr Melnychenko

Khmelnytskyi National University

Liliana Klymenko

Shupyk National Healthcare University of Ukraine

Oleksii Kovalchuk

Khmelnytskyi National University

Cooperative Institutions

Khmelnytskyi National University

Taras Shevchenko National University of Kyiv

Ministry of Education and Science

V.M. Glushkov Institute of Cybernetics

Cracow University of Technology

European Organization for Nuclear Research

Gdańsk University of Technology

British Academy

National University Odesa Law Academy

Odessa National Economics University

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Detection of Early Pneumonia on Individual CT Scans with Dilated Convolutions.

Юрий Крак Olexander Barmak Pavlo Radiuk

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APPLICATION OF A GENETIC ALGORITHM TO SEARCH FOR THE OPTIMAL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE WITH WEIGHT DISTRIBUTION

Herald of Khmelnytskyi National University Technical sciences (2020)

Pavlo Radiuk

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.

Genetic architecture

10.31891/2307-5732-2020-281-1-7-11

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A Framework for Exploring and Modelling Neural Architecture Search Methods

COLINS (2020)

Pavlo Radiuk Nadiia Hrypynska

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Citations (6)