Continuous Cartesian Genetic Programming based representation for multi-objective neural architecture search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{GARCIAGARCIA:2023:asoc,
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author = "Cosijopii Garcia-Garcia and Alicia Morales-Reyes and
Hugo Jair Escalante",
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title = "Continuous Cartesian Genetic Programming based
representation for multi-objective neural architecture
search",
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journal = "Applied Soft Computing",
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volume = "147",
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pages = "110788",
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year = "2023",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2023.110788",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494623008062",
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keywords = "genetic algorithms, genetic programming, Neural
architecture search, ANN, Cartesian genetic
programming, Convolutional neural network,
Multi-objective optimization",
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abstract = "We propose a novel neural architecture search (NAS)
approach for the challenge of designing convolutional
neural networks (CNNs) that achieve a good tradeoff
between complexity and accuracy. We rely on Cartesian
genetic programming (CGP) and integrated real-based and
block-chained CNN representation, for optimization
using multi-objective evolutionary algorithms (MOEAs)
in the continuous domain. We introduce two variants,
CGP-NASV1 and CGP-NASV2, which differ in the
granularity of their respective search spaces. To
evaluate the proposed algorithms, we used the
non-dominated sorting genetic algorithm II (NSGA-II) on
the CIFAR-10, CIFAR-100,and SVHN datasets.
Additionally, we extended the empirical analysis while
maintaining the same solution representation to assess
other searching techniques such as differential
evolution (DE), the multi-objective evolutionary
algorithm based on decomposition (MOEA/D), and the S
metric selection evolutionary multi-objective algorithm
(SMS-EMOA). The experimental results demonstrate that
our approach exhibits competitive classification
performance and model complexity compared to
state-of-the-art methods",
- }
Genetic Programming entries for
Cosijopii Garcia-Garcia
Alicia Morales-Reyes
Hugo Jair Escalante
Citations