Sum Epsilon-Tube Error Fitness Function Design for GP Symbolic Regression: Preliminary Study
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- @InProceedings{Matousek:2019:ICCAIRO,
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author = "Radomil Matousek and Tomas Hulka and
Ladislav Dobrovsky and Jakub Kudela",
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title = "Sum Epsilon-Tube Error Fitness Function Design for
{GP} Symbolic Regression: Preliminary Study",
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booktitle = "2019 International Conference on Control, Artificial
Intelligence, Robotics Optimization (ICCAIRO)",
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year = "2019",
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pages = "78--83",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICCAIRO47923.2019.00021",
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abstract = "Symbolic Regression (SR) is a well-studied method in
Genetic Programming (GP) for discovering free-form
mathematical models from observed data, which includes
not only the model parameters but also its innate
structure. Another level of the regression problem is
the design of an appropriate fitness function, by which
are individual solutions judged. This paper proposes a
new fitness function design for symbolic regression
problems called a Sum epsilon-Tube Error (STE). The
function of this criterion can be visualized as a tube
with a small radius that stretches along the entire
domain of the approximated function. The middle of the
tube is defined by points that match approximated
valued (in the so-called control points). The
evaluation function then compares, whether each
approximated point does or does not belong to the area
of the tube and counts the number of points outside of
the epsilon-Tube. The proposed method is compared with
the standard sum square error in several test cases,
where the advantages and disadvantages of the design
are discussed. The obtained results show great promise
for the further development of the STE design and
implementation.",
-
notes = "Also known as \cite{9057172}",
- }
Genetic Programming entries for
Radomil Matousek
Tomas Hulka
Ladislav Dobrovsky
Jakub Kudela
Citations