Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{POLONSKAIA:2021:PCS,

• author = "Iana S. Polonskaia and Ilya R. Aliev and Nikolay O. Nikitin",
• title = "Automated Evolutionary Design of {CNN} Classifiers for Object Recognition on Satellite Images",
• journal = "Procedia Computer Science",
• volume = "193",
• pages = "210--219",
• year = "2021",
• note = "10th International Young Scientists Conference in Computational Science, YSC2021, 28 June - 2 July, 2021",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2021.10.021",
• URL = "https://www.sciencedirect.com/science/article/pii/S1877050921020627",
• keywords = "genetic algorithms, genetic programming, evolutionary learning, NAS, CNN, machine learning, recognition, satellite images",
• abstract = "In the paper, the automated evolutionary approach FEDOT-NAS for the design of convolutional neural networks is proposed. It allows building object recognition models for remote sensing tasks. The comparison of the proposed approach with state-of-the-art tools for neural architecture search is conducted for several examples of satellite-related datasets. The results of the experiments confirm the correctness and effectiveness of the proposed approach. The implementation of FEDOT-NAS is available as an open-source tool",

}

Genetic Programming entries for Iana S Polonskaia Ilya R Aliev Nikolay O Nikitin

Citations