# Tensorized NeuroEvolution of Augmenting Topologies for GPU Acceleration

arXiv: https://arxiv.org/abs/2404.01817 · HTML: https://mlsanity.com/paper/2404.01817/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2024.

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

- [Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration](https://mlsanity.com/paper/2501.17168/) (2025)
- [Tensor-Accelerated Eager Multi-Resolution Grids for Evolving Large-Scale Substrates](https://mlsanity.com/paper/2608.27612/) (2026)
- [When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?](https://mlsanity.com/paper/2505.22696/) (2025)
- [Teaching the Teacher: The Role of Teacher-Student Smoothness Alignment in Genetic Programming-based Symbolic Distillation](https://mlsanity.com/paper/2507.22767/) (2025)
- [On Scaling Coordinate-Based Neuroevolution: The Quadtree Bottleneck in ES-HyperNEAT](https://mlsanity.com/paper/2608.24480/) (2026)

## Abstract (arXiv)

The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with simple networks and incrementally evolving both their topologies and weights. Although its capability across various challenges is evident, the algorithm's computational efficiency remains an impediment, limiting its scalability potential. In response, this paper introduces a tensorization method for the NEAT algorithm, enabling the transformation of its diverse network topologies and associated operations into uniformly shaped tensors for computation. This advancement facilitates the execution of the NEAT algorithm in a parallelized manner across the entire population. Furthermore, we develop TensorNEAT, a library that implements the tensorized NEAT algorithm and its variants, such as CPPN and HyperNEAT. Building upon JAX, TensorNEAT promotes efficient parallel computations via automated function vectorization and hardware acceleration. Moreover, the TensorNEAT library supports various benchmark environments including Gym, Brax, and gymnax. Through evaluations across a spectrum of robotics control environments in Brax, TensorNEAT achieves up to 500x speedups compared to the existing implementations such as NEAT-Python. Source codes are available at: https://github.com/EMI-Group/tensorneat.