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GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

Nathan Haut, Ilya Basin, Marzieh Kianinejad, Ruchika Gupta, Elijah Smith, Zachary Perrico, Wolfgang Banzhaf

Abstract

Beagle is a new software framework that enables execution of Genetic Programming tasks on the GPU. Currently available for symbolic regression, it processes individuals of the population and fitness cases for training in a way that maximizes throughput on extant GPU platforms. In this contribution, we report on the benchmarking of Beagle on the Feynman Symbolic Regression dataset and compare its performance with a fast CPU system called StackGP and the widely available PySR system under the same wall clock budget. We also report on the use of two different fitness functions, one a point-to-point error function, the other a correlation fitness function. The results demonstrate that the Beagle's GPU-aided Symbolic Regression significantly outperforms leading CPU-based frameworks.

GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

Abstract

Beagle is a new software framework that enables execution of Genetic Programming tasks on the GPU. Currently available for symbolic regression, it processes individuals of the population and fitness cases for training in a way that maximizes throughput on extant GPU platforms. In this contribution, we report on the benchmarking of Beagle on the Feynman Symbolic Regression dataset and compare its performance with a fast CPU system called StackGP and the widely available PySR system under the same wall clock budget. We also report on the use of two different fitness functions, one a point-to-point error function, the other a correlation fitness function. The results demonstrate that the Beagle's GPU-aided Symbolic Regression significantly outperforms leading CPU-based frameworks.
Paper Structure (16 sections, 3 equations, 2 figures, 5 tables)

This paper contains 16 sections, 3 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Beagle Framework
  4. C# and ILGPU
  5. Approaches for GPU Code
  6. Approaches for CPU Code
  7. GPU-Friendly Bespoke Language
  8. Fitness Functions
  9. Population Size
  10. Parameters
  11. StackGP
  12. PySR
  13. Benchmark Setup
  14. Results
  15. Conclusion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: The 10 minute benchmarking performance of the different symbolic regression methods. The darker shaded regions indicate the typical performance ($>=50\%$ solved), whereas the lighter region indicates the number of problems solved at least once.
  • Figure 2: The 30 minute benchmarking performance of the different symbolic regression methods. The darker shaded regions indicate the typical performance ($>=50\%$ solved), whereas the lighter region indicates the number of problems solved at least once.