🌟Distributed GPU-accelerated Framework for Scalable Evolutionary Computation🌟

---

Building upon JAX and Ray, EvoX offers a comprehensive suite of 50+ Evolutionary Algorithms (EAs) and a wide range of 100+ Benchmark Problems, 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.

---

Key Features

• 🚀 Fast Performance:

  • Experience GPU-Accelerated optimization, achieving speeds 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, ensuring robust testing and evaluation.
  • Explore the frontier of AI with extensive tools for Neuroevolution tasks.

• 🛠️ Designed for Simplicity:

  • Embrace the elegance of Functional Programming, simplifying complex algorithmic compositions.
  • Benefit from Hierarchical State Management, ensuring modular and clean programming.
  • Jumpstart your journey with our Detailed Tutorial.

---

Comprehensive Evolutionary Algorithms

Single-Objective Optimization

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

Multi-Objective Optimization

Category	Algorithm Names
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, ...

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

Diverse Benchmark Problems

Category	Problem Names
Numerical	DTLZ, LSMOP, MaF, ZDT, CEC'22, ...
Neuroevolution	Brax, Gym, TorchVision Dataset, ...

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

Setting Up EvoX

Install evox effortlessly via pip:

pip install evox

Note: To install EvoX with JAX and hardware acceleration capabilities, please refer to our comprehensive Installation Guide.

Quick Start

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)

Use-cases and Applications

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.

Community & Support

• 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.

Sister Projects

• 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.

Citing EvoX

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 = {arXiv preprint arXiv:2301.12457},
  eprint = {2301.12457},
  year = {2023}
}