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

Research Center Hagenberg

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

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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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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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.

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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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