# TensorGP -- Genetic Programming Engine in TensorFlow

arXiv: https://arxiv.org/abs/2103.07512 · HTML: https://mlsanity.com/paper/2103.07512/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning](https://mlsanity.com/paper/1903.01855/) (2019)
- [TensorFlow Enabled Genetic Programming](https://mlsanity.com/paper/1708.03157/) (2017)

## Built on by (most cited first, 5 of 5)

- [Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming](https://mlsanity.com/paper/2111.07875/) (2021)
- [Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration](https://mlsanity.com/paper/2501.17168/) (2025)
- [Accelerating Genetic Programming using GPUs](https://mlsanity.com/paper/2110.11226/) (2021)
- [Speed Benchmarking of Genetic Programming Frameworks](https://mlsanity.com/paper/2106.11919/) (2021)
- [Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming](https://mlsanity.com/paper/2609.03352/) (2026)

## Abstract (arXiv)

In this paper, we resort to the TensorFlow framework to investigate the benefits of applying data vectorization and fitness caching methods to domain evaluation in Genetic Programming. For this purpose, an independent engine was developed, TensorGP, along with a testing suite to extract comparative timing results across different architectures and amongst both iterative and vectorized approaches. Our performance benchmarks demonstrate that by exploiting the TensorFlow eager execution model, performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware when compared to a standard iterative approach.