Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Wittenberg:2023:GPEM,
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author = "David Wittenberg and Franz Rothlauf and
Christian Gagne",
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title = "Denoising autoencoder genetic programming: strategies
to control exploration and exploitation in search",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2023",
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volume = "24",
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number = "2",
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pages = "Article number: 17",
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month = dec,
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Estimation of
distribution algorithms, EDA, ANN, LSTM, Probabilistic
model-building, Denoising autoencoders",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/dqFao",
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DOI = "doi:10.1007/s10710-023-09462-2",
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size = "27 pages",
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abstract = "Denoising autoencoder genetic programming (DAE-GP) is
a novel neural network-based estimation of distribution
genetic programming approach that uses denoising
autoencoder long short-term memory networks as a
probabilistic model to replace the standard mutation
and recombination operators of genetic programming. At
each generation, the idea is to capture promising
properties of the parent population in a probabilistic
model and to use corruption to transfer variations of
these properties to the offspring. This work studies
the influence of corruption and sampling steps on
search. Corruption partially mutates candidate
solutions that are used as input to the model, whereas
the number of sampling steps defines how often we
re-use the output during model sampling as input to the
model. We study the generalization of the royal tree
problem, the Airfoil problem, and the Pagie-1 problem,
and find that both corruption strength and the number
of sampling steps influence exploration and
exploitation in search and affect performance:
exploration increases with stronger corruption and
lower number of sampling steps. The results indicate
that both corruption and sampling steps are key to the
success of the DAE-GP: it permits us to balance the
exploration and exploitation behavior in search,
resulting in an improved search quality. However, also
selection is important for exploration and exploitation
and should be chosen wisely.",
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notes = "Johannes Gutenberg University, Mainz, Rhineland
Palatinate, Germany",
- }
Genetic Programming entries for
David Wittenberg
Franz Rothlauf
Christian Gagne
Citations