Designing neural networks through neuroevolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Stanley:2019:NMI,
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author = "Kenneth O. Stanley and Jeff Clune and Joel Lehman and
Risto Miikkulainen",
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title = "Designing neural networks through neuroevolution",
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journal = "Nature Machine Intelligence",
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year = "2019",
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volume = "1",
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pages = "24--35",
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month = "7 " # jan,
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keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming",
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URL = "https://www.nature.com/articles/s42256-018-0006-z?fbclid=IwAR0v_oJR499daqgqiKCAMa-LHWAoRYuaiTpOtHCws0Wmc6vcbe5Qx6Yjils",
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DOI = "doi:10.1038/s42256-018-0006-z",
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size = "12 pages",
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abstract = "Much of recent machine learning has focused on deep
learning, in which neural network weights are trained
through variants of stochastic gradient descent. An
alternative approach comes from the field of
neuroevolution, which harnesses evolutionary algorithms
to optimize neural networks, inspired by the fact that
natural brains themselves are the products of an
evolutionary process. Neuroevolution enables important
capabilities that are typically unavailable to
gradient-based approaches, including learning neural
network building blocks (for example activation
functions), hyperparameters, architectures and even the
algorithms for learning themselves. Neuroevolution also
differs from deep learning (and deep reinforcement
learning) by maintaining a population of solutions
during search, enabling extreme exploration and massive
parallelisation. Finally, because neuroevolution
research has (until recently) developed largely in
isolation from gradient-based neural network research,
it has developed many unique and effective techniques
that should be effective in other machine learning
areas too. This Review looks at several key aspects of
modern neuroevolution, including large-scale computing,
the benefits of novelty and diversity, the power of
indirect encoding, and the field's contributions to
meta-learning and architecture search. Our hope is to
inspire renewed interest in the field as it meets the
potential of the increasing computation available
today, to highlight how many of its ideas can provide
an exciting resource for inspiration and hybridization
to the deep learning, deep reinforcement learning and
machine learning communities, and to explain how
neuroevolution could prove to be a critical tool in the
long-term pursuit of artificial general intelligence.",
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notes = "Artificial Intelligence Review.
Uber AI Labs, San Francisco, CA, USA.",
- }
Genetic Programming entries for
Kenneth O Stanley
Jeff Clune
Joel Lehman
Risto Miikkulainen
Citations