Hybrid symbolic regression with the Bison Seeker algorithm

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid symbolic regression with the Bison Seeker algorithm

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Hybrid symbolic regression with the Bison Seeker algorithm

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Mendel

ISBN

—

ISSN

1803-3814

e-ISSN

2571-3701

Počet stran výsledku

8

Strana od-do

79-86

Název nakladatele

Vysoké učení technické v Brně

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

10. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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