Multi-Gene Grammatical Evolution for Symbolic Regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00231186" target="_blank" >RIV/68407700:21230/15:00231186 - isvavai.cz</a>
Result on the web
<a href="http://radio.feld.cvut.cz/conf/poster2015/Proceedings_of_Poster_2015.zip" target="_blank" >http://radio.feld.cvut.cz/conf/poster2015/Proceedings_of_Poster_2015.zip</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Multi-Gene Grammatical Evolution for Symbolic Regression
Original language description
Genetic Programming is now a mature technology for solving difficult non-linear regression tasks, where other approaches often fail to produce good solutions. However, there are still issues with the performance of GP in symbolic regression tasks. Recentresearch has shown the power of utilizing linear regression and multiple expression trees to create more accurate solutions. Grammatical Evolution is a variant of GP which uses grammar to create the solutions, but no research so far has investigated thepossibility of using multiple genes in it. In this article we propose a multi-gene algorithm based upon Grammatical Evolution and show its superior performance on a classical symbolic regression benchmark.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 19th International Scientific Student Conferenece POSTER 2015
ISBN
978-80-01-05499-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
Czech Technical University in Prague
Place of publication
Praha
Event location
Praha
Event date
May 14, 2015
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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