Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming

被引：4
|
作者：
Randall, David L. [1 ]
Townsend, Tyler S. [2 ]
Hochhalter, Jacob D. [1 ]
Bomarito, Geoffrey F. [3 ]
机构：
[1] Univ Utah, Salt Lake City, UT 84112 USA
[2] Microsoft, Redmond, WA USA
[3] NASA Langley Res Ctr, Hampton, VA USA
来源：
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 | 2022年
关键词：
genetic programming; symbolic regression; genetic programming for symbolic regression;
D O I：
10.1145/3520304.3534031
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：2282 / 2288
页数：7
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