# GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming

arXiv: https://arxiv.org/abs/2106.04034 · HTML: https://mlsanity.com/paper/2106.04034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Faster GPU Based Genetic Programming Using A Two Dimensional Stack](https://mlsanity.com/paper/1601.00221/) (2016)

## Built on by (most cited first, 1 of 1)

- [Kozax: Flexible and Scalable Genetic Programming in JAX](https://mlsanity.com/paper/2502.03047/) (2025)

## Abstract (arXiv)

Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently then operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000X relative to the state-of-the-art sequential implementation.