evox 0.5.0

evox

EvoX is a distributed GPU-accelerated framework for scalable evolutionary computation. Our primary goal is to push the boundaries of evolutionary computation by significantly enhancing its speed and versatility, enabling its application to complex and computationally intensive tasks.

⭐️ Key Features

• 🚀 Fast
  • GPU computing for 10x-100x faster optimization.
  • Distributed workflow for even faster optimization.
• 🌟 Wide support
  • Single-objective and multi-objective optimization.
  • Comprehensive support for commonly used benchmark problems.
  • Extensive coverage of neuroevolution problems.
• 🎉 Easy to use
  • Functional programming for easy function composition.
  • Hierarchical state management for modular programming.
  • Detailed tutorial available here.

EvoX offers a powerful and user-friendly optimization framework, empowering researchers and practitioners to easily tackle a variety of optimization tasks. The support for commonly used benchmark problems, along with the coverage of neuroevolution problems, provides a versatile platform for optimization experimentation. With its fast GPU computing and distributed workflow capabilities, EvoX enables efficient optimization of complex and computationally intensive problems. The functional programming and hierarchical state management further enhance the ease of use and modularity of the framework.

Index

• Contents
  • List of Algorithms
    • Single-objective
    • Multi-objective
  • List of Problems
• Installation
• Quick Start
• Example
• Support
• Citation

Contents

List of Algorithms

Single-objective

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

Multi-objective

Type	Algorithm Name
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, ...

List of Problems

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

For more detailed list, please refer to our API documentation. List of Algorithms and List of Problems.

Installation

We recommand install evox using pip

pip install evox

EvoX depends on JAX. To install JAX, please refer to JAX's installation guide here.

Quick Start

To start with, import evox

import evox
from evox import algorithms, problems, workflows

Then, create an algorithm and 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()

To run the EC workflow, compose the algorithm and the problem together using workflow:

workflow = workflows.StdWorkflow(pso, ackley)

To initialize the whole workflow, call init on the workflow object with a PRNGKey. Calling init will recursively initialize a tree of objects, meaning the algorithm pso and problem ackley are automatically initialize as well.

key = jax.random.PRNGKey(42)
state = workflow.init(key)

Now, call step to execute one iteration of the workflow.

# run the workflow for 100 steps
for i in range(100):
    state = workflow.step(state)

Example

The example folder has many examples on how to use EvoX.

Support

• For general discussion, please head to Github's discussion
• For Chinese speakers, please consider to join the QQ group to discuss. (Group number: 297969717).

Citation

@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}
}