Bingo
David Randall, Tyler Townsend, Jacob Hochhalter, Geoffrey Bomarito
2022Proceedings of the Genetic and Evolutionary Computation Conference Companion13 citationsDOIOpen Access PDF
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.
Topics & Concepts
Symbolic regressionComputer scienceProgramming languageGenetic programmingPython (programming language)PersonalizationModular designParallel computingTheoretical computer scienceArtificial intelligenceWorld Wide WebEvolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchAdvanced Multi-Objective Optimization Algorithms