Building upon JAX and Ray, EvoX offers a comprehensive suite of 50+ Evolutionary Algorithms (EAs) and a wide range of 100+ Benchmark Problems/Environments, all benefiting from distributed GPU-acceleration. It facilitates efficient exploration of complex optimization landscapes, effective tackling of black-box optimization challenges, and deep dives into neuroevolution with Brax. With a foundation in functional programming and hierarchical state management, EvoX offers a user-friendly and modular experience. For more details, please refer to our Paper and Documentation / 文档.

• 🚀 Fast Performance:

  • Experience GPU-Accelerated optimization, achieving speeds over 100x faster than traditional methods.
  • Leverage the power of Distributed Workflows for even more rapid optimization.

• 🌐 Versatile Optimization Suite:

  • Cater to all your needs with both Single-objective and Multi-objective optimization capabilities.
  • Dive into a comprehensive library of Benchmark Problems/Environments, ensuring robust testing and evaluation.
  • Explore the frontier of AI with extensive tools for Neuroevolution/RL tasks.

• 🛠️ Designed for Simplicity:

  • Embrace the elegance of Functional Programming, simplifying complex algorithmic compositions.
  • Benefit from Hierarchical State Management, ensuring modular and clean programming.

For a comprehensive list and further details of all algorithms, please check the API Documentation.

For a comprehensive list and further details of all benchmark problems/environments, please check the API Documentation.

Install evox effortlessly via pip:

pip install evox

Note: To setup EvoX with GPU acceleration capabilities, you will need to setup JAX first. For detials, please refer to our comprehensive Installation Guide. Additionally, you can watch our instructional videos:

🎥 EvoX Installation Guide (Linux)

🎥 EvoX Installation Guide (Windows)

🎥 EvoX 安装指南 (Linux)

🎥 EvoX 安装指南 (Windows)

Kickstart your journey with EvoX in just a few simple steps:

1. Import necessary modules:

import evox
from evox import algorithms, problems, workflows

2. Configure an algorithm and define a problem:

pso = algorithms.PSO(
    lb=jnp.full(shape=(2,), fill_value=-32),
    ub=jnp.full(shape=(2,), fill_value=32),
    pop_size=100,
)
ackley = problems.numerical.Ackley()

3. Compose and initialize the workflow:

workflow = workflows.StdWorkflow(pso, ackley)
key = jax.random.PRNGKey(42)
state = workflow.init(key)

4. Run the workflow:

# Execute the workflow for 100 iterations
for i in range(100):
    state = workflow.step(state)

Try out ready-to-play examples in your browser with Colab:

Example	Link
Basic Usage
Numerical Optimization
Neuroevolution with Gym
Neuroevolution with Brax
Custom Algorithm/Problem

For more use-cases and applications, pleae check out Example Directory.

• Engage in discussions and share your experiences on GitHub Discussion Board.
• Join our QQ group (ID: 297969717).
• Help with the translation of the documentation on Weblate. We currently support translations in two languages, English / 中文.
• Official Website: https://evox.group/

• 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 benchmark platform for Neural Architecutre Search (NAS) without the requirement of GPUs/PyTorch/Tensorflow, supporting various programming languages such as Java, Matlab, Python, ect. Check out here.

If you use EvoX in your research and want to cite it in your work, please use:

@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}
}