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Bingo: a customizable framework for symbolic regression with genetic programming

Authors:

David L. Randall,

Tyler S. Townsend,

Jacob D. Hochhalter,

Geoffrey F. BomaritoAuthors Info & Claims

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

Pages 2282 - 2288

https://doi.org/10.1145/3520304.3534031

Published: 19 July 2022 Publication History

Abstract

In this paper, we introduce Bingo, a flexible and customizable yet performant Python framework for symbolic regression with genetic programming. Bingo maintains a modular code structure for simple abstraction and easily swappable components. Fitness functions, selection methods, and constant optimization methods allow for easy problem-specific customization. Bingo also maintains several features for increased efficiency such as parallelism, equation simplification, and a C++ backend. We compare Bingo's performance to other genetic programming for symbolic regression (GPSR) methods to show that it is both competitive and flexible.

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Cited By

Garbrecht KRovinelli AHochhalter JChristodoulou PLebensohn RCapolungo L(2024)Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approachMechanics of Materials10.1016/j.mechmat.2024.105151199(105151)Online publication date: Dec-2024
https://doi.org/10.1016/j.mechmat.2024.105151
Hansen CWhelan GHochhalter J(2024)Interpretable machine learning for microstructure-dependent models of fatigue indicator parametersInternational Journal of Fatigue10.1016/j.ijfatigue.2023.108019178(108019)Online publication date: Jan-2024
https://doi.org/10.1016/j.ijfatigue.2023.108019
Oh HAmici RBomarito GZhe SKirby RHochhalter J(2024)Inherently interpretable machine learning solutions to differential equationsEngineering with Computers10.1007/s00366-023-01915-740:4(2349-2361)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00366-023-01915-7
Show More Cited By

Index Terms

Bingo: a customizable framework for symbolic regression with genetic programming
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Abstraction, modeling and modularity

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Published In

cover image ACM Conferences

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

Copyright © 2022 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

Publication History

Published: 19 July 2022

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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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Cited By

Garbrecht KRovinelli AHochhalter JChristodoulou PLebensohn RCapolungo L(2024)Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approachMechanics of Materials10.1016/j.mechmat.2024.105151199(105151)Online publication date: Dec-2024
https://doi.org/10.1016/j.mechmat.2024.105151
Hansen CWhelan GHochhalter J(2024)Interpretable machine learning for microstructure-dependent models of fatigue indicator parametersInternational Journal of Fatigue10.1016/j.ijfatigue.2023.108019178(108019)Online publication date: Jan-2024
https://doi.org/10.1016/j.ijfatigue.2023.108019
Oh HAmici RBomarito GZhe SKirby RHochhalter J(2024)Inherently interpretable machine learning solutions to differential equationsEngineering with Computers10.1007/s00366-023-01915-740:4(2349-2361)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00366-023-01915-7
Kronberger GOlivetti de Franca FDesmond HBartlett DKammerer L(2024)The Inefficiency of Genetic Programming for Symbolic RegressionParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_17(273-289)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_17
de Franca FKronberger GSilva SPaquete L(2023)Reducing Overparameterization of Symbolic Regression Models with Equality SaturationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590346(1064-1072)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590346
Garbrecht KBirky DLester BEmery JHochhalter J(2023)Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regressionJournal of the Mechanics and Physics of Solids10.1016/j.jmps.2023.105472181(105472)Online publication date: Dec-2023
https://doi.org/10.1016/j.jmps.2023.105472

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