Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming

David L Randall; Tyler S Townsend; Jacob D Hochhalter; Geoffrey F Bomarito

NASA NTRS · 20220005435

Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming

David L Randall·Tyler S Townsend·Jacob D Hochhalter·Geoffrey F Bomarito

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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BibTeX RIS

David L Randall, Tyler S Townsend, Jacob D Hochhalter, Geoffrey F Bomarito. Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming. https://ntrs.nasa.gov/citations/20220005435

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