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Analyzing optimized constants in genetic programming on a real-world regression problem

Authors:

Dominik Sobania,

Martin Briesch,

David Wittenberg,

Franz RothlaufAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 606 - 607

https://doi.org/10.1145/3520304.3528896

Published: 19 July 2022 Publication History

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Abstract

Ephemeral random constants are commonly used for symbolic regression with genetic programming. However, due to their random nature, it is difficult for genetic programming to find proper constants. This often leads to large and complex non-interpretable solutions. Therefore, in this short paper, we analyze a method that replaces ephemeral random constants with constant tokens whose values are optimized during evolution with the Sequential Least Squares Programming method. The results achieved on the studied regression problem indicate that replacing ephemeral random constants by optimized constant tokens leads to solutions with a significantly lower error which are also smaller than the solutions generated by a standard GP approach using ephemeral random constants.

References

[1]

Paulo Cortez, António Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009. Modeling wine preferences by data mining from physicochemical properties. Decision support systems 47, 4 (2009), 547--553.

Google Scholar

[2]

Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner Gardner, Marc Parizeau, and Christian Gagné. 2012. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research 13, 1 (2012), 2171--2175.

Digital Library

Google Scholar

[3]

Michael Kommenda, Bogdan Burlacu, Gabriel Kronberger, and Michael Affenzeller. 2020. Parameter identification for symbolic regression using nonlinear least squares. Genetic Programming and Evolvable Machines 21, 3 (2020), 471--501.

Digital Library

Google Scholar

[4]

Michael Kommenda, Gabriel Kronberger, Stephan Winkler, Michael Affenzeller, and Stefan Wagner. 2013. Effects of constant optimization by nonlinear least squares minimization in symbolic regression. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. 1121--1128.

Digital Library

Google Scholar

[5]

John R Koza. 1992. Genetic programming: on the programming of computers by means of natural selection. Vol. 1. MIT press.

Digital Library

Google Scholar

[6]

Dieter Kraft et al. 1988. A software package for sequential quadratic programming. (1988).

Google Scholar

[7]

Michael A Lones, Jane E Alty, Jeremy Cosgrove, Philippa Duggan-Carter, Stuart Jamieson, Rebecca F Naylor, Andrew J Turner, and Stephen L Smith. 2017. A new evolutionary algorithm-based home monitoring device for Parkinson's dyskinesia. Journal of medical systems 41, 11 (2017), 1--8.

Digital Library

Google Scholar

[8]

Ji Ni, Russ H Drieberg, and Peter I Rockett. 2012. The use of an analytic quotient operator in genetic programming. IEEE Transactions on Evolutionary Computation 17, 1 (2012), 146--152.

Digital Library

Google Scholar

[9]

Dominik Sobania, Martin Briesch, and Franz Rothlauf. 2022. Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference.

Digital Library

Google Scholar

[10]

Alexander Topchy, William F Punch, et al. 2001. Faster genetic programming based on local gradient search of numeric leaf values. In Proceedings of the genetic and evolutionary computation conference (GECCO-2001), Vol. 155162. Morgan Kaufmann.

Google Scholar

Cited By

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Chen QXue BBanzhaf WZhang MSilva SPaquete L(2023)Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear ScalingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3595918(420-428)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3595918

Index Terms

Analyzing optimized constants in genetic programming on a real-world regression problem
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Automatic programming
        Genetic programming

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Published In

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2022

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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

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Chen QXue BBanzhaf WZhang MSilva SPaquete L(2023)Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear ScalingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3595918(420-428)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3595918

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Recommendations

Differential evolution of constants in genetic programming improves efficacy and bloat

Nonlinear Least Squares Optimization of Constants in Symbolic Regression

Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression