poster

Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees

Authors:

Mohammad Mehdi EbadzadehAuthors Info & Claims

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1441 - 1442

https://doi.org/10.1145/2739482.2764662

Published: 11 July 2015 Publication History

Get Access

Abstract

Genetic programming (GP) is widely used for constructing models with applications in control, classification, regression, etc.; however, it has some shortcomings, such as generalization. This paper proposes to enhance the GP generalization by controlling the first order derivative of GP trees in the evolution process. To achieve this goal, a multi-objective GP is implemented. Then, the first order derivative of GP trees is considered as one of its objectives. The proposed method is evaluated on several benchmark problems to provide an experimental validation. The experiments demonstrate the usefulness of the proposed method with the capability of achieving compact solutions with reasonable accuracy on training data and better accuracy on test data.

References

[1]

M. A. Haeri, M. M. Ebadzadeh, and G. Folino. Improving gp generalization: a variance-based layered learning approach. Genetic Programming and Evolvable Machines, 16(1):27--55, 2015.

Digital Library

Google Scholar

[2]

J. Ni, R. H. Drieberg, and P. I. Rockett. The use of an analytic quotient operator in genetic programming. Evolutionary Computation, IEEE Transactions on, 17(1):146--152, 2013.

Digital Library

Google Scholar

[3]

J. Ni and P. I. Rockett. Tikhonov regularization as a complexity measure in multiobjective genetic programming. Evolutionary Computation, IEEE Transactions on, 19(2):157--166, 2015.

Google Scholar

[4]

N. Y. Nikolaev and H. Iba. Regularization approach to inductive genetic programming. Evolutionary Computation, IEEE Transactions on, 5(4):359--375, 2001.

Digital Library

Google Scholar

[5]

L. Vanneschi, M. Castelli, and S. Silva. Measuring bloat, overfitting and functional complexity in genetic programming. In Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 877--884. ACM, 2010.

Digital Library

Google Scholar

[6]

E. J. Vladislavleva, G. F. Smits, and D. Den Hertog. Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. Evolutionary Computation, IEEE Transactions on, 13(2):333--349, 2009.

Digital Library

Google Scholar

Cited By

View all

Fan LSu ZLiu XWang Y(2024)Decomposition based cross-parallel multiobjective genetic programming for symbolic regressionApplied Soft Computing10.1016/j.asoc.2024.112239167:PAOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112239
Wang CChen QXue BZhang M(2023)Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic RegressionData Science and Machine Learning10.1007/978-981-99-8696-5_12(163-176)Online publication date: 5-Dec-2023
https://doi.org/10.1007/978-981-99-8696-5_12
Raymond CChen QXue BZhang MCoello Coello C(2020)Adaptive weighted splinesProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390244(1003-1011)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390244
Show More Cited By

Recommendations

Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and ...
Measuring bloat, overfitting and functional complexity in genetic programming
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Recent contributions clearly show that eliminating bloat in a genetic programming system does not necessarily eliminate overfitting and vice-versa. This fact seems to contradict a common agreement of many researchers known as the minimum description ...
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Semantic Backpropagation (SB) is a recent technique that promotes effective variation in tree-based genetic programming. The basic idea of SB is to provide information on what output is desirable for a specified tree node, by propagating the desired ...

Comments

Information & Contributors

Information

Published In

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

Check for updates

Author Tags

Qualifiers

Poster

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
186
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fan LSu ZLiu XWang Y(2024)Decomposition based cross-parallel multiobjective genetic programming for symbolic regressionApplied Soft Computing10.1016/j.asoc.2024.112239167:PAOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112239
Wang CChen QXue BZhang M(2023)Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic RegressionData Science and Machine Learning10.1007/978-981-99-8696-5_12(163-176)Online publication date: 5-Dec-2023
https://doi.org/10.1007/978-981-99-8696-5_12
Raymond CChen QXue BZhang MCoello Coello C(2020)Adaptive weighted splinesProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390244(1003-1011)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390244
Chen QZhang MXue B(2019)Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic RegressionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2018.288139223:4(703-717)Online publication date: Aug-2019
https://doi.org/10.1109/TEVC.2018.2881392
Chen QXue BZhang M(2019)Improving Generalization of Genetic Programming for Symbolic Regression With Angle-Driven Geometric Semantic OperatorsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2018.286962123:3(488-502)Online publication date: Jun-2019
https://doi.org/10.1109/TEVC.2018.2869621
Rockett PLopes YDou THathway E(2019)d(Tree)-by-dx: Automatic and Exact Differentiation of Genetic Programming TreesHybrid Artificial Intelligent Systems10.1007/978-3-030-29859-3_12(133-144)Online publication date: 4-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-29859-3_12
Liu QHu Y(2019)A Discrete Cosine Transform Based Evolutionary Algorithm and Its Application for Symbolic RegressionIntelligent Computing10.1007/978-3-030-22871-2_30(444-462)Online publication date: 23-Jun-2019
https://doi.org/10.1007/978-3-030-22871-2_30
Chen QZhang MXue B(2017)Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic RegressionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.268348921:5(792-806)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1109/TEVC.2017.2683489
Chen QXue BShang LZhang MNeumann FSutton A(2016)Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk MinimisationProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908842(709-716)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908842
Chen QXue BNiu BZhang M(2016)Improving generalisation of genetic programming for high-dimensional symbolic regression with feature selection2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744270(3793-3800)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7744270
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations

Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression

Measuring bloat, overfitting and functional complexity in genetic programming

Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations