Thesis Details

Porovnání variant genetického programování v úloze symbolické regrese

Bachelor's Thesis Student: Doležal Petr Academic Year: 2021/2022 Supervisor: Drahošová Michaela, Ing., Ph.D.
English title
Comparison of Genetic Programming Variants in the Symbolic Regression Task
Language
Czech
Abstract

This thesis deals with comparison of genetic programming variants it the task of symbolic regression. Time to converge and quality of evolved solutions are evaluated on nine chosen benchmarks. In particular, tree-based genetic programming, cartesian genetic programming and their modifications using coevolutionary algorithm are investigated. An own implementation of employed methods (without a specific library use) allows to share as much code as possible. Moreover, an analysis of implemented methods efficiency on real world data is provided. Experimental results show that all of the investigated approaches are capable of finding solutions using symbolic regression. Cartesian genetic programming enhanced with coevolution seems to be the most suitable of the investigated approaches in terms of evolved solution quality and time to converge.

Keywords

Symbolic regression, genetic programming, cartesian genetic programming, coevolution.

Department
Degree Programme
Files
Status
defended, grade B
Date
15 June 2022
Reviewer
Committee
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT), předseda
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Hynek Jiří, Ing., Ph.D. (DIFS FIT BUT), člen
Kekely Lukáš, Ing., Ph.D. (DCSY FIT BUT), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Citation
DOLEŽAL, Petr. Porovnání variant genetického programování v úloze symbolické regrese. Brno, 2022. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2022-06-15. Supervised by Drahošová Michaela. Available from: https://www.fit.vut.cz/study/thesis/24804/
BibTeX
@bachelorsthesis{FITBT24804,
    author = "Petr Dole\v{z}al",
    type = "Bachelor's thesis",
    title = "Porovn\'{a}n\'{i} variant genetick\'{e}ho programov\'{a}n\'{i} v \'{u}loze symbolick\'{e} regrese",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2022,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/24804/"
}
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