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Digital
Library of the European Council for Modelling and Simulation |
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Title: |
Different Approaches For Constant Estimation In
Analytic Programming |
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Authors: |
Zuzana Kominkova Oplatkova, Adam Viktorin, Roman
Senkerik, Tomas Urbanek |
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Published in:
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(2017).ECMS 2017 Proceedings
Edited by: Zita Zoltay
Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi:10.7148/2017 ISBN:
978-0-9932440-4-9/ ISBN:
978-0-9932440-5-6 (CD) 31st European Conference on Modelling and Simulation, Budapest, Hungary, May 23rd
– May 26th, 2017 |
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Citation
format: |
Zuzana Kominkova Oplatkova,
Adam Viktorin, Roman Senkerik,
Tomas Urbanek (2017). Different Approaches For
Constant Estimation In Analytic Programming, ECMS 2017 Proceedings Edited by:
Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council
for Modeling and Simulation. doi:
10.7148/2017-0326 |
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DOI: |
https://doi.org/10.7148/2017-0326 |
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Abstract: |
This
research deals with different approaches for constant estimation in analytic
programming (AP). AP is a tool for symbolic regression tasks
which enables to synthesise an analytical
solution based on the required behaviour of the
system. Some tasks do not need any constant estimation - AP is used in its
basic version without any constant estimation handling. Compared to this,
cases like data approximation need constants (coefficients)
which are essential for the process of precise solution synthesis.
This paper offers another strategy to already known and used by the AP from
the very beginning and approaches published recently in 2016. This paper
compares these procedures and the discussion also includes nonlinear fitting
and metaevolutionary approach. As the main
evolutionary algorithm, a differential algorithm (de/rand/1/bin) for the main
process of AP is used. |
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