Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem

Erik Pitzer; Gabriel Kronberger

doi:10.1007/978-3-319-27340-2_47

Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem

Erik Pitzer^*
, Gabriel Kronberger

^*Korrespondierende/r Autor/-in für diese Arbeit

Research Center Hagenberg

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

1 Zitat (Scopus)

Abstract

The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems.

Originalsprache	Englisch
Titel	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Redakteure/-innen	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Herausgeber (Verlag)	Springer
Seiten	375-383
Seitenumfang	9
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_47
Publikationsstatus	Veröffentlicht - 2015
Veranstaltung	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spanien Dauer: 8 Feb. 2015 → 13 Feb. 2015 http://eurocast2015.fulp.ulpgc.es/

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	9520
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Konferenz

Konferenz	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Land/Gebiet	Spanien
Ort	Las Palmas, Gran Canaria
Zeitraum	08.02.2015 → 13.02.2015
Internetadresse	http://eurocast2015.fulp.ulpgc.es/

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10.1007/978-3-319-27340-2_47

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Pitzer, E., & Kronberger, G. (2015). Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem. in F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Hrsg.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (S. 375-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9520). Springer. https://doi.org/10.1007/978-3-319-27340-2_47

Pitzer, Erik ; Kronberger, Gabriel. / Smooth symbolic regression : Transformation of symbolic regression into a real-valued optimization problem. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Hrsg. / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. S. 375-383 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem",

abstract = "The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems.",

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booktitle = "Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers",

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Pitzer, E & Kronberger, G 2015, Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem. in F Pichler, R Moreno-Díaz & A Quesada-Arencibia (Hrsg.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 9520, Springer, S. 375-383, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spanien, 08.02.2015. https://doi.org/10.1007/978-3-319-27340-2_47

Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem. / Pitzer, Erik ; Kronberger, Gabriel.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Hrsg. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. S. 375-383 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9520).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

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T2 - 15th International Conference on Computer Aided Systems Theory, Eurocast 2015

AU - Pitzer, Erik

AU - Kronberger, Gabriel

PY - 2015

Y1 - 2015

N2 - The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems.

AB - The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems.

UR - https://www.scopus.com/pages/publications/84952329752

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DO - 10.1007/978-3-319-27340-2_47

M3 - Conference contribution

AN - SCOPUS:84952329752

SN - 9783319273396

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BT - Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers

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Pitzer E , Kronberger G. Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem. in Pichler F, Moreno-Díaz R, Quesada-Arencibia A, Hrsg., Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. S. 375-383. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27340-2_47

Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem

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