AUTOMATIC GENERATION OF NEURAL NETWORK ARCHITECTURES BY MULTI-OBJECTIVE GENETIC ALGORITHM : научное издание

Edreev Vladimir; Stanovov Vladimir

Дополнительные ссылки: eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: AUTOMATIC GENERATION OF NEURAL NETWORK ARCHITECTURES BY MULTI-OBJECTIVE GENETIC ALGORITHM : научное издание / V. Edreev, V. Stanovov. - Текст : непосредственный // Facta Universitatis, Series: Mathematics and Informatics. - 2025. - Т. 40, № 4. - С. 873. - ISSN 0352-9665. - ISSN 2406-047X, DOI 10.22190/fumi241010061e.

Аннотация: The widespread introduction of artificial neural networks leads to the need to create a variety of architectures for solving various problems. Optimization methods are actively used to automate this process. This process is called Neural architecture search. Among them the greatest variety of models is given by multi-objective optimization algorithms based on simultaneous optimization of all criteria. An important part of the algorithms is the way the neural networks are encoded. A flexible scheme of encoding and decoding of architectures-individuals by binary strings was proposed: each part of a binary string is responsible for a parameter or a whole block (layer, layer set) of a neural network. This approach allows to create neural networks with different architectures within one run of the algorithm. The use of the notion of strict dominance has also been investigated, computational experiments have shown that the use of strict dominance gives a statistically significant increase in hypervolume metric.

Год издания: 2025

Авторы: Edreev Vladimir, Stanovov Vladimir

Издательство: Белград

Источник: Facta Universitatis, Series: Mathematics and Informatics

Выпуск: Т. 40, № 4

Номера страниц: 873

Количество экземпляров:

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