Hybrid Single Node Genetic Programming for Symbolic Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00316259" target="_blank" >RIV/68407700:21730/16:00316259 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/16:00316259

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-662-53525-7_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-662-53525-7_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-662-53525-7_4" target="_blank" >10.1007/978-3-662-53525-7_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid Single Node Genetic Programming for Symbolic Regression

Popis výsledku v původním jazyce

This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact version of the best-performing graph to the beginning and to the end of the population, respectively, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on five symbolic regression benchmarks and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to improve the performance of the SNGP algorithm. We then propose two variants of hybrid SNGP utilizing a linear regression technique, LASSO, to improve its performance. The proposed algorithms have been compared to the state-of-the-art symbolic regression methods that also make use of the linear regression techniques on four real-world benchmarks. The results show the hybrid SNGP algorithms are at least competitive with or better than the compared methods. Springer-Verlag Berlin Heidelberg 2016.

Název v anglickém jazyce

Hybrid Single Node Genetic Programming for Symbolic Regression

Popis výsledku anglicky

This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact version of the best-performing graph to the beginning and to the end of the population, respectively, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on five symbolic regression benchmarks and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to improve the performance of the SNGP algorithm. We then propose two variants of hybrid SNGP utilizing a linear regression technique, LASSO, to improve its performance. The proposed algorithms have been compared to the state-of-the-art symbolic regression methods that also make use of the linear regression techniques on four real-world benchmarks. The results show the hybrid SNGP algorithms are at least competitive with or better than the compared methods. Springer-Verlag Berlin Heidelberg 2016.

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

<a href="/cs/project/GA15-22731S" target="_blank" >GA15-22731S: Symbolická regrese pro posilované učení ve spojitých prostorech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Transactions on Computational Collective Intelligence XXIV

ISBN

978-3-662-53524-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

22

Strana od-do

61-82

Název nakladatele

Springer Berlin Heidelberg

Místo vydání

Berlin

Místo konání akce

Lisabon

Datum konání akce

12. 11. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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