# EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation

arXiv: https://arxiv.org/abs/2301.12457 · HTML: https://mlsanity.com/paper/2301.12457/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2023.

## Built on by (most cited first, 18 of 18)

- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch](https://mlsanity.com/paper/2409.02969/) (2024)
- [MetaDE: Evolving Differential Evolution by Differential Evolution](https://mlsanity.com/paper/2502.10470/) (2025)
- [MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization](https://mlsanity.com/paper/2505.17745/) (2025)
- [Rethinking Population-assisted Off-policy Reinforcement Learning](https://mlsanity.com/paper/2305.02949/) (2023)
- [Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration](https://mlsanity.com/paper/2501.17168/) (2025)
- [Kozax: Flexible and Scalable Genetic Programming in JAX](https://mlsanity.com/paper/2502.03047/) (2025)
- [Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization](https://mlsanity.com/paper/2603.18388/) (2026)
- [A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping](https://mlsanity.com/paper/2503.21797/) (2025)
- [MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics](https://mlsanity.com/paper/2603.09237/) (2026)
- [EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2501.15129/) (2025)
- [PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization](https://mlsanity.com/paper/2503.22722/) (2025)
- [A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation](https://mlsanity.com/paper/2404.16266/) (2024)
- [SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking](https://mlsanity.com/paper/2505.17430/) (2025)
- [Dual-Domain Representation Alignment: Bridging 2D and 3D Vision via Geometry-Aware Architecture Search](https://mlsanity.com/paper/2603.19563/) (2026)
- [Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data](https://mlsanity.com/paper/2605.29058/) (2026)
- [Towards Efficient Evaluation of Evolutionary Transfer Optimization: Case Studies on Task-Parameterized Applications](https://mlsanity.com/paper/2609.05040/) (2026)
- [Autoresearch in Mixed-Integer Linear and Nonlinear Programming](https://mlsanity.com/paper/2609.39360/) (2026)

## Abstract (arXiv)

Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. Recently, with the surge in data-intensive applications and large-scale complex systems, the demand for scalable EC solutions has grown significantly. However, most existing EC infrastructures fall short of catering to the heightened demands of large-scale problem solving. While the advent of some pioneering GPU-accelerated EC libraries is a step forward, they also grapple with some limitations, particularly in terms of flexibility and architectural robustness. In response, we introduce EvoX: a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms. At the core of EvoX lies a unique programming model to streamline the development of parallelizable EC algorithms, complemented by a computation model specifically optimized for distributed GPU acceleration. Building upon this foundation, we have crafted an extensive library comprising a wide spectrum of 50+ EC algorithms for both single- and multi-objective optimization. Furthermore, the library offers comprehensive support for a diverse set of benchmark problems, ranging from dozens of numerical test functions to hundreds of reinforcement learning tasks. Through extensive experiments across a range of problem scenarios and hardware configurations, EvoX demonstrates robust system and model performances. EvoX is open-source and accessible at: https://github.com/EMI-Group/EvoX.