Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Misc{defranca2023interpretablesymbolicregressiondata,
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author = "F. O. {de Franca} and M. Virgolin and M. Kommenda and
M. S. Majumder and M. Cranmer and G. Espada and
L. Ingelse and A. Fonseca and M. Landajuela and
B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and
J. D. Hochhalter and D. L. Randall and P. Kamienny and
H. Zhang and G. Dick and A. Simon and B. Burlacu and
Jaan Kasak and Meera Machado and Casper Wilstrup and
W. G. {La Cava}",
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title = "Interpretable Symbolic Regression for Data Science:
Analysis of the 2022 Competition",
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howpublished = "arXiv 2304.01117",
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year = "2023",
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month = "3 " # jul,
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note = "v3",
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Competition, Interpretable Machine
Learning, XAI",
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eprint = "2304.01117",
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archiveprefix = "arXiv",
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primaryclass = "cs.LG",
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URL = "https://arxiv.org/abs/2304.01117",
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size = "13 pages",
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abstract = "Symbolic regression searches for analytic expressions
that accurately describe studied phenomena. The main
attraction of this approach is that it returns an
interpretable model that can be insightful to users.
Historically, the majority of algorithms for symbolic
regression have been based on evolutionary algorithms.
However, there has been a recent surge of new proposals
that instead use approaches such as enumeration
algorithms, mixed linear integer programming, neural
networks, and Bayesian optimization. In order to assess
how well these new approaches behave on a set of common
challenges often faced in real-world data, we hosted a
competition at the 2022 Genetic and Evolutionary
Computation Conference consisting of different
synthetic and real-world datasets which were blind to
entrants. For the real-world track, we assessed
interpretability in a realistic way by using a domain
expert to judge the trustworthiness of candidate this
http URL present an in-depth analysis of the results
obtained in this competition, discuss current
challenges of symbolic regression algorithms and
highlight possible improvements for future
competitions.",
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notes = "submitted to IEEE Transactions on Evolutionary
Computation \cite{deFranca:ieeeTEC}
GECCO-2022",
- }
Genetic Programming entries for
Fabricio Olivetti de Franca
Marco Virgolin
Michael Kommenda
Maimuna Majumder
Miles Cranmer
Guilherme Jorge Nunes Monteiro Espada
Leon Ingelse
Alcides Fonseca
Mikel Landajuela
Brenden Kyle Petersen
Ruben Glatt
T Nathan Mundhenk
Chak Shing Lee
Jacob Dean Hochhalter
David L Randall
Pierre-Alexandre Kamienny
Hengzhe Zhang
Grant Dick
Alessandro Simon
Bogdan Burlacu
Jaan Kasak
Meera Machado
Casper Wilstrup
William La Cava
Citations