An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning
Joint Authors
Mattioli, Fernando
Caetano, Daniel
Cardoso, Alexandre
Naves, Eduardo
Lamounier, Edgard
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
The choice of a good topology for a deep neural network is a complex task, essential for any deep learning project.
This task normally demands knowledge from previous experience, as the higher amount of required computational resources makes trial and error approaches prohibitive.
Evolutionary computation algorithms have shown success in many domains, by guiding the exploration of complex solution spaces in the direction of the best solutions, with minimal human intervention.
In this sense, this work presents the use of genetic algorithms in deep neural networks topology selection.
The evaluated algorithms were able to find competitive topologies while spending less computational resources when compared to state-of-the-art methods.
American Psychological Association (APA)
Mattioli, Fernando& Caetano, Daniel& Cardoso, Alexandre& Naves, Eduardo& Lamounier, Edgard. 2019. An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1173700
Modern Language Association (MLA)
Mattioli, Fernando…[et al.]. An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1173700
American Medical Association (AMA)
Mattioli, Fernando& Caetano, Daniel& Cardoso, Alexandre& Naves, Eduardo& Lamounier, Edgard. An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1173700
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173700