Multiobjective Evolution for Convolutional Neural Network Architecture Search
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00534830" target="_blank" >RIV/67985807:_____/20:00534830 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-61401-0_25" target="_blank" >http://dx.doi.org/10.1007/978-3-030-61401-0_25</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-61401-0_25" target="_blank" >10.1007/978-3-030-61401-0_25</a>
Alternative languages
Result language
angličtina
Original language name
Multiobjective Evolution for Convolutional Neural Network Architecture Search
Original language description
The choice of an architecture is crucial for the performance of the neural network, and thus automatic methods for architecture search have been proposed to provide a data-dependent solution to this problem. In this paper, we deal with an automatic neural architecture search for convolutional neural networks. We propose a novel approach for architecture selection based on multi-objective evolutionary optimisation. Our algorithm optimises not only the performance of the network, but it controls also the size of the network, in terms of the number of network parameters. The proposed algorithm is evaluated on experiments, including MNIST and fashionMNIST classification problems. Our approach outperforms both the considered baseline architectures and the standard genetic algorithm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Intelligence and Soft Computing. ICAISC 2020 Proceedings, Part I
ISBN
978-3-030-61400-3
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
261-270
Publisher name
Springer
Place of publication
Cham
Event location
Zakopane
Event date
Oct 12, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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