Use of two-layer genetic programming for multidimensional symbolic regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F24%3A39922640" target="_blank" >RIV/00216275:25530/24:39922640 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10900766" target="_blank" >https://ieeexplore.ieee.org/document/10900766</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/Informatics62280.2024.10900766" target="_blank" >10.1109/Informatics62280.2024.10900766</a>

Result language
angličtina
Original language name
Use of two-layer genetic programming for multidimensional symbolic regression
Original language description
This paper focuses on exploring the potential benefits and advantages or disadvantages of a two-layer approach in genetic programming. The first section describes two-layer genetic programming itself and how it differs from its basic version. The Python programming language framework DEAP was used for the implementation. The focus of the paper is also to compare the results obtained by using this two-layer genetic programming with different configurations of the parameters with ordinary basic genetic programming on different multidimensional datasets and benchmarks.
Czech name
—
Czech description
—

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Informatics 2024 : 2024 IEEE 17th International Scientific Conference on Informatics : proceedings
ISBN
979-8-3503-8766-7
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
503-508
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
New York
Event location
Poprad
Event date
Nov 13, 2024
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
001483035700082

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