Hybrid symbolic regression with the Bison Seeker algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F19%3A39915379" target="_blank" >RIV/00216275:25530/19:39915379 - isvavai.cz</a>
Result on the web
<a href="https://mendel-journal.org/index.php/mendel/article/view/82/109" target="_blank" >https://mendel-journal.org/index.php/mendel/article/view/82/109</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/mendel.2019.1.079" target="_blank" >10.13164/mendel.2019.1.079</a>
Alternative languages
Result language
angličtina
Original language name
Hybrid symbolic regression with the Bison Seeker algorithm
Original language description
This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression. While the basic version of symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. This paper aims to compare the basic version of symbolic regression and hybrid version with the lifetime adaptation of individuals via the Bison Seeker Algorithm. Author also investigates the influence of the Bison Seeker Algorithm on the rate of evolution in the search for function, which fits the given input-output data. The results of the current study support the fact that the local algorithm accelerates evolution, even with a few iterations of a Bison Seeker Algorithm with small populations.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Mendel
ISBN
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ISSN
1803-3814
e-ISSN
2571-3701
Number of pages
8
Pages from-to
79-86
Publisher name
Vysoké učení technické v Brně
Place of publication
Brno
Event location
Brno
Event date
Jul 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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