Lifetime Adaptation in Genetic Programming for the Symbolic Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F19%3A39915385" target="_blank" >RIV/00216275:25530/19:39915385 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-31362-3_33" target="_blank" >http://dx.doi.org/10.1007/978-3-030-31362-3_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-31362-3_33" target="_blank" >10.1007/978-3-030-31362-3_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Lifetime Adaptation in Genetic Programming for the Symbolic Regression

Popis výsledku v původním jazyce

This paper focuses on the use of hybrid genetic programming for the supervised machine learning method called symbolic regression. While the basic version of GP 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. Choice of local learning method can accelerate the evolution, but it also has its disadvantages in the form of additional costs. Strong local learning can inhibit the evolutionary search for the optimal genotype due to the hiding effect, in which the fitness of the individual only slightly depends on his inherited genes. This paper aims to compare the Lamarckian and Baldwinian approaches to the lifetime adaptation of individuals and their influence on the rate of evolution in the search for function, which fits the given input-output data.

Název v anglickém jazyce

Lifetime Adaptation in Genetic Programming for the Symbolic Regression

Popis výsledku anglicky

This paper focuses on the use of hybrid genetic programming for the supervised machine learning method called symbolic regression. While the basic version of GP 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. Choice of local learning method can accelerate the evolution, but it also has its disadvantages in the form of additional costs. Strong local learning can inhibit the evolutionary search for the optimal genotype due to the hiding effect, in which the fitness of the individual only slightly depends on his inherited genes. This paper aims to compare the Lamarckian and Baldwinian approaches to the lifetime adaptation of individuals and their influence on the rate of evolution in the search for function, which fits the given input-output data.

Druh

D - Stať ve sborníku

CEP obor

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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

Computational statistics and mathematical modeling methods in intelligent systems : proceedings of 3rd computational methods in systems and software 2019, Vol. 2

ISBN

978-3-030-31361-6

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

8

Strana od-do

339-346

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Zlín

Datum konání akce

10. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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