Evolutionary NAS with Gene Expression Programming of Cellular Encoding
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Broni-Bediako:2020:SSCI,
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author = "Cliford Broni-Bediako and Yuki Murata and
Luiz H. B. Mormille and Masayasu Atsumi",
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title = "Evolutionary NAS with Gene Expression Programming of
Cellular Encoding",
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booktitle = "2020 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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year = "2020",
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pages = "2670--2676",
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abstract = "The renaissance of neural architecture search (NAS)
has seen classical methods such as genetic algorithms
(GA) and genetic programming (GP) being exploited for
convolutional neural network (CNN) architectures. While
recent work have achieved promising performance on
visual perception tasks, the direct encoding scheme of
both GA and GP has functional complexity deficiency and
does not scale well on large architectures like CNN. To
address this, we present a new generative encoding
scheme-symbolic linear generative encoding
(SLGE)-simple, yet a powerful scheme which embeds local
graph transformations in chromosomes of linear
fixed-length string to develop CNN architectures of
variant shapes and sizes via an evolutionary process of
gene expression programming. In experiments, the
effectiveness of SLGE is shown in discovering
architectures that improve the performance of the
state-of-the-art handcrafted CNN architectures on
CIFAR-10 and CFAR-100 image classification tasks; and
achieves a competitive classification error rate with
the existing NAS methods using fewer GPU resources.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI47803.2020.9308346",
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month = dec,
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notes = "Also known as \cite{9308346}",
- }
Genetic Programming entries for
Cliford Broni-Bediako
Yuki Murata
Luiz Henrique Barbosa Mormille
Masayasu Atsumi
Citations