Lifetime Adaptation in Genetic Programming for the Symbolic Regression
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Lifetime Adaptation in Genetic Programming for the Symbolic Regression
Original language description
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.
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
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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Number of pages
8
Pages from-to
339-346
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Zlín
Event date
Sep 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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