Enhanced Symbolic Regression Through Local Variable Transformations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00316262" target="_blank" >RIV/68407700:21230/17:00316262 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/17:00316262
Result on the web
<a href="http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006505200910100" target="_blank" >http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006505200910100</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0006505200910100" target="_blank" >10.5220/0006505200910100</a>
Alternative languages
Result language
angličtina
Original language name
Enhanced Symbolic Regression Through Local Variable Transformations
Original language description
Genetic programming (GP) is a technique widely used in a range of symbolic regression problems, in particular when there is no prior knowledge about the symbolic function sought. In this paper, we present a GP extension introducing a new concept of local transformed variables, based on a locally applied affine transformation of the original variables. This approach facilitates finding accurate parsimonious models. We have evaluated the proposed extension in the context of the Single Node Genetic Programming (SNGP) algorithm on synthetic as well as real-problem datasets. The results confirm our hypothesis that the transformed variables significantly improve the performance of the standard SNGP algorithm.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 9th International Joint Conference on Computational Intelligence
ISBN
978-989-758-274-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
91-100
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Funchal, Madeira
Event date
Nov 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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