Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InCollection{Suganuma:2020:DNE,
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author = "Masanori Suganuma and Shinichi Shirakawa and
Tomoharu Nagao",
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title = "Designing Convolutional Neural Network Architectures
Using Cartesian Genetic Programming",
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booktitle = "Deep Neural Evolution: Deep Learning with Evolutionary
Computation",
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publisher = "Springer, Singapore",
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year = "2020",
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editor = "Hitoshi Iba and Nasimul Noman",
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series = "Natural Computing Series",
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chapter = "7",
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pages = "185--208",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, ANN",
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isbn13 = "978-981-15-3684-7",
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DOI = "doi:10.1007/978-981-15-3685-4_7",
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abstract = "Convolutional neural networks (CNNs), among the deep
learning models, are making remarkable progress in a
variety of computer vision tasks, such as image
recognition, restoration, and generation. The network
architecture in CNNs should be manually designed in
advance. Researchers and practitioners have developed
various neural network structures to improve
performance. Despite the fact that the network
architecture considerably affects the performance, the
selection and design of architectures are tedious and
require trial-and-error because the best architecture
depends on the target task and amount of data.
Evolutionary algorithms have been successfully applied
to automate the design process of CNN architectures.
This chapter aims to explain how evolutionary
algorithms can support the automatic design of CNN
architectures. We introduce a method based on Cartesian
genetic programming (CGP) for the design of CNN
architectures. CGP is a form of genetic programming and
searches the network-structured program. We represent
the CNN architecture via a combination of pre-defined
modules and search for the high-performing architecture
based on CGP. The method attempts to find better
architectures by repeating the architecture generation,
training, and evaluation. The effectiveness of the
CGP-based CNN architecture search is demonstrated
through two types of computer vision tasks: image
classification and image restoration. The experimental
result for image classification shows that the method
can find a well-performing CNN architecture. For the
experiment on image restoration tasks, we show that the
method can find a simple yet high-performing
architecture of a convolutional autoencoder that is a
type of CNN.",
- }
Genetic Programming entries for
Masanori Suganuma
Shinichi Shirakawa
Tomoharu Nagao
Citations