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

^*Corresponding author for this work

Research Center Hagenberg

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Editors	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Publisher	Springer
Pages	375-383
Number of pages	9
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_47
Publication status	Published - 2015
Event	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain Duration: 8 Feb 2015 → 13 Feb 2015 http://eurocast2015.fulp.ulpgc.es/

Publication series

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

Conference

Conference	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Country/Territory	Spain
City	Las Palmas, Gran Canaria
Period	08.02.2015 → 13.02.2015
Internet address	http://eurocast2015.fulp.ulpgc.es/

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

Cite this

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 (Eds.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (pp. 375-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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. editor / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. pp. 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.",

author = "Erik Pitzer and Gabriel Kronberger",

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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 (eds), 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), vol. 9520, Springer, pp. 375-383, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spain, 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. ed. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. p. 375-383 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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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.

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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, editors, Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. p. 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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