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🌟Distributed GPU-accelerated Framework for Scalable Evolutionary Computation🌟

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🔥 News

• [2025-03-01] Released EvoX 1.1.0 - now switched to torch.compile (TorchDynamo) [Details]
• [2025-02-03] Released EvoRL: A GPU-accelerated framework for Evolutionary Reinforcement Learning, powered by JAX ! [Paper] [Code]
• [2025-01-30] Released EvoGP: A GPU-accelerated framework for Genetic Programming, powered by PyTorch & CUDA! [Paper] [Code]
• [2025-01-14] Released EvoX 1.0.0 - now fully compatible with PyTorch, with full torch.compile support! Users of the previous JAX-based version can access it on the v0.9.0 branch.

Table of Contents

1. Overview
2. Key Features
3. Main Contents
4. Quick Installation
5. Sister Projects
6. Community & Support

Overview

EvoX is a distributed GPU-accelerated evolutionary computation framework compatible with PyTorch*. With a user-friendly programming model, it offers a comprehensive suite of 50+ Evolutionary Algorithms (EAs) and a wide range of 100+ Benchmark Problems/Environments. For more details, please refer to our Paper and Documentation / 文档.

*Users of the previous JAX-based version can access it on the v0.9.0 branch.

Key Features

💻 High-Performance Computing

🚀 Ultra Performance

• Supports acceleration on heterogeneous hardware, including both CPUs and GPUs, achieving over 100x speedups.
• Integrates distributed workflows that scale seamlessly across multiple nodes or devices.

🌐 All-in-One Solution

• Includes 50+ algorithms for a wide range of use cases, fully supporting single- and multi-objective optimization.
• Provides a hierarchical architecture for complex tasks such as meta learning, hyperparameter optimization, and neuroevolution.

🛠️ Easy-to-Use Design

• Fully compatible with PyTorch and its ecosystem, simplifying algorithmic development with a tailored programming model.
• Ensures effortless setup with one-click installation for Windows users.

📊 Versatile Benchmarking

📚 Extensive Benchmark Suites

• Features 100+ benchmark problems spanning single-objective optimization, multi-objective optimization, and real-world engineering challenges.

🎮 Support for Physics Engines

• Integrates seamlessly with physics engines like Brax and other popular frameworks for reinforcement learning.

⚙️ Customizable Problems

• Provides an encapsulated module for defining and evaluating custom problems tailored to user needs, with seamless integration into real-world applications and datasets.

📈 Flexible Visualization

🔍 Ready-to-Use Tools

• Offers a comprehensive set of visualization tools for analyzing evolutionary processes across various tasks.

🛠️ Customizable Modules

• Enables users to integrate their own visualization code, allowing for tailored and flexible visualizations.

📂 Real-Time Data Streaming

• Leverages the tailored .exv format to simplify and accelerate real-time data streaming.

Main Contents

Evolutionary Algorithms for Single-objective Optimization

Category	Algorithms
Differential Evolution	CoDE, JaDE, SaDE, SHADE, IMODE, ...
Evolution Strategy	CMA-ES, PGPE, OpenES, CR-FM-NES, xNES, ...
Particle Swarm Optimization	FIPS, CSO, CPSO, CLPSO, SL-PSO, ...

Evolutionary Algorithms for Multi-objective Optimization

Category	Algorithms
Dominance-based	NSGA-II, NSGA-III, SPEA2, BiGE, KnEA, ...
Decomposition-based	MOEA/D, RVEA, t-DEA, MOEAD-M2M, EAG-MOEAD, ...
Indicator-based	IBEA, HypE, SRA, MaOEA-IGD, AR-MOEA, ...

Benchmark Problems/Environments

Category	Problems/Environments
Numerical	DTLZ, LSMOP, MaF, ZDT, CEC'22, ...
Neuroevolution/RL	Brax, TorchVision Dataset, ...

For a comprehensive list and detailed descriptions of all algorithms, please check the Algorithms API, and for benchmark problems/environments, refer to the Problems API.

Quick Installation

Install evox effortlessly via pip:

pip install evox

Note: Windows users can use the win-install.bat script for installation.

Sister Projects

• EvoRL:GPU-accelerated framework for Evolutionary Reinforcement Learning. Check out here.
• EvoGP:GPU-accelerated framework for Genetic Programming. Check out here.
• TensorNEAT: Tensorized NeuroEvolution of Augmenting Topologies (NEAT) for GPU Acceleration. Check out here.
• TensorRVEA: Tensorized Reference Vector Guided Evolutionary Algorithm (RVEA) for GPU Acceleration. Check out here.
• TensorACO: Tensorized Ant Colony Optimization (ACO) for GPU Acceleration. Check out here.
• EvoXBench: A real-world benchmark platform for solving various optimization problems, such as Neural Architecture Search (NAS). It operates without the need for GPUs/PyTorch/TensorFlow and supports multiple programming environments. Check out here.

Stay tuned - more exciting developments are on the way! ✨

Community & Support

• Join discussions on the GitHub Discussion Board.
• Connect via Discord or QQ group (ID: 297969717).
• Help translate EvoX docs on Weblate. We currently support translations in two languages, English / 中文.

Citing EvoX

If EvoX contributes to your research, please cite it:

@article{evox,
  title = {{EvoX}: {A} {Distributed} {GPU}-accelerated {Framework} for {Scalable} {Evolutionary} {Computation}},
  author = {Huang, Beichen and Cheng, Ran and Li, Zhuozhao and Jin, Yaochu and Tan, Kay Chen},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2024,
  doi = {10.1109/TEVC.2024.3388550}
}

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