Hybrid Single Node Genetic Programming for Symbolic Regression

Result code in 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>

Alternative codes found

RIV/68407700:21230/16:00316259

Result on the web

<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>

Result language

angličtina

Original language name

Hybrid Single Node Genetic Programming for Symbolic Regression

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA15-22731S" target="_blank" >GA15-22731S: Symbolic Regression for Reinforcement Learning in Continuous Spaces</a><br>

Continuities

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

Publication year

2016

Confidentiality

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

Article name in the collection

Transactions on Computational Collective Intelligence XXIV

ISBN

978-3-662-53524-0

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

22

Pages from-to

61-82

Publisher name

Springer Berlin Heidelberg

Place of publication

Berlin

Event location

Lisabon

Event date

Nov 12, 2015

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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