Small Solutions for Real-World Symbolic Regression using Denoising Autoencoder Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Wittenberg:2023:EuroGP,
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author = "David Wittenberg and Franz Rothlauf",
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title = "Small Solutions for Real-World Symbolic Regression
using Denoising Autoencoder Genetic Programming",
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booktitle = "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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year = "2023",
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month = "12-14 " # apr,
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editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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series = "LNCS",
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volume = "13986",
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publisher = "Springer Verlag",
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address = "Brno, Czech Republic",
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pages = "101--116",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Estimation of
Distribution Algorithms, EDA, Denoising Autoencoders,
Symbolic Regression",
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isbn13 = "978-3-031-29572-0",
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URL = "https://rdcu.be/c8UQO",
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DOI = "doi:10.1007/978-3-031-29573-7_7",
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size = "16 pages",
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abstract = "Denoising Autoencoder Genetic Programming (DAE-GP) is
a model-based evolutionary algorithm that uses
denoising autoencoder long short-term memory networks
as probabilistic model to replace the standard
recombination and mutation operators of genetic
programming (GP). In this paper, we use the DAE-GP to
solve a set of nine standard real-world symbolic
regression tasks. We compare the prediction quality of
the DAE-GP to standard GP, geometric semantic GP
(GSGP), and the gene-pool optimal mixing evolutionary
algorithm for GP (GOMEA-GP), and find that the DAE-GP
shows similar prediction quality using a much lower
number of fitness evaluations than GSGP or GOMEA-GP. In
addition, the DAE-GP consistently finds small
solutions. The best candidate solutions of the DAE-GP
are 69percent smaller (median number of nodes) than the
best candidate solutions found by standard GP. An
analysis of the bias of the selection and variation
step for both the DAE-GP and standard GP gives insight
into why differences in solution size exist: the strong
increase in solution size for standard GP is a result
of both selection and variation bias. The results
highlight that learning and sampling from a
probabilistic model is a promising alternative to
classic GP variation operators where the DAE-GP is able
to generate small solutions for real-world symbolic
regression tasks.",
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notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in
conjunction with EvoCOP2023, EvoMusArt2023 and
EvoApplications2023",
- }
Genetic Programming entries for
David Wittenberg
Franz Rothlauf
Citations