On the Effectiveness of Genetic Operations in Symbolic Regression

Bogdan Burlacu; Michael Affenzeller; Michael Kommenda

doi:10.1007/978-3-319-27340-2_46

On the Effectiveness of Genetic Operations in Symbolic Regression

Bogdan Burlacu, Michael Affenzeller, Michael Kommenda

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

Abstract

This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.

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	367-374
Number of pages	8
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_46
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/

Keywords

Genetic programming
evolutionary dynamics
algorithm analysis
symbolic regression
Symbolic regression
Evolutionary dynamics
Algorithm analysis

Access to Document

10.1007/978-3-319-27340-2_46

Cite this

Burlacu, B., Affenzeller, M., & Kommenda, M. (2015). On the Effectiveness of Genetic Operations in Symbolic Regression. In F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (pp. 367-374). (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_46

Burlacu, Bogdan ; Affenzeller, Michael ; Kommenda, Michael. / On the Effectiveness of Genetic Operations in Symbolic Regression. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. editor / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. pp. 367-374 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "On the Effectiveness of Genetic Operations in Symbolic Regression",

abstract = "This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.",

keywords = "Genetic programming, evolutionary dynamics, algorithm analysis, symbolic regression, Genetic programming, evolutionary dynamics, algorithm analysis, symbolic regression, Symbolic regression, Evolutionary dynamics, Algorithm analysis",

author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; 15th International Conference on Computer Aided Systems Theory, Eurocast 2015 ; Conference date: 08-02-2015 Through 13-02-2015",

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doi = "10.1007/978-3-319-27340-2\_46",

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editor = "Franz Pichler and Roberto Moreno-D{\'i}az and Alexis Quesada-Arencibia",

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

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Burlacu, B , Affenzeller, M & Kommenda, M 2015, On the Effectiveness of Genetic Operations in Symbolic Regression. 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. 367-374, 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_46

On the Effectiveness of Genetic Operations in Symbolic Regression. / Burlacu, Bogdan ; Affenzeller, Michael; Kommenda, Michael.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. ed. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. p. 367-374 (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

TY - GEN

T1 - On the Effectiveness of Genetic Operations in Symbolic Regression

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AU - Affenzeller, Michael

AU - Kommenda, Michael

PY - 2015

Y1 - 2015

N2 - This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.

AB - This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.

KW - Genetic programming

KW - evolutionary dynamics

KW - algorithm analysis

KW - symbolic regression

KW - Genetic programming

KW - evolutionary dynamics

KW - algorithm analysis

KW - symbolic regression

KW - Symbolic regression

KW - Evolutionary dynamics

KW - Algorithm analysis

UR - https://www.scopus.com/pages/publications/84952360511

U2 - 10.1007/978-3-319-27340-2_46

DO - 10.1007/978-3-319-27340-2_46

M3 - Conference contribution

SN - 9783319273396

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 367

EP - 374

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

A2 - Pichler, Franz

A2 - Moreno-Díaz, Roberto

A2 - Quesada-Arencibia, Alexis

PB - Springer

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

Y2 - 8 February 2015 through 13 February 2015

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Burlacu B , Affenzeller M, Kommenda M. On the Effectiveness of Genetic Operations in Symbolic Regression. 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. 367-374. (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_46

On the Effectiveness of Genetic Operations in Symbolic Regression

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