Neuroevolution with box mutation: An adaptive and modular framework for evolving deep neural networks
Created by W.Langdon from
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- @Article{SANTOS:2023:asoc,
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author = "Frederico J. J. B. Santos and Ivo Goncalves and
Mauro Castelli",
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title = "Neuroevolution with box mutation: An adaptive and
modular framework for evolving deep neural networks",
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journal = "Applied Soft Computing",
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volume = "147",
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pages = "110767",
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year = "2023",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2023.110767",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494623007858",
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keywords = "genetic algorithms, genetic programming,
Neuroevolution, ANN, Evolutionary deep learning, Neural
architecture search, Supervised learning",
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abstract = "The pursuit of self-evolving neural networks has
driven the emerging field of Evolutionary Deep
Learning, which combines the strengths of Deep Learning
and Evolutionary Computation. This work presents a
novel method for evolving deep neural networks by
adapting the principles of Geometric Semantic Genetic
Programming, a subfield of Genetic Programming, and
Semantic Learning Machine. Our approach integrates
evolution seamlessly through natural selection with the
optimization power of backpropagation in deep learning,
enabling the incremental growth of neural networks'
neurons across generations. By evolving neural networks
that achieve nearly 89percent accuracy on the CIFAR-10
dataset with relatively few parameters, our method
demonstrates remarkable efficiency, evolving in GPU
minutes compared to the field standard of GPU days",
- }
Genetic Programming entries for
Frederico J J B Santos
Ivo Goncalves
Mauro Castelli
Citations