EvoToolkit¶

LLM-driven solution evolutionary optimization toolkit

EvoToolkit is a Python library that leverages Large Language Models (LLMs) to evolve solutions for optimization problems. It combines the power of evolutionary algorithms with LLM-based solution generation and refinement, supporting code, text, and other evaluable representations.

✨ Key Features¶

🤖 LLM-Driven Evolution: Use state-of-the-art language models to generate and evolve solutions
🔬 Multiple Algorithms: Support for EoH, EvoEngineer, and FunSearch evolutionary methods
🌍 Task-Agnostic: Supports any evaluable optimization task (code, text, math expressions, etc.)
🎯 Extensible Framework: Easy-to-extend task system for custom optimization problems
🔌 Simple API: High-level evotoolkit.solve() function for quick prototyping
🛠️ Advanced Customization: Low-level API for fine-grained control

Built-in Task Types¶

Task Type	Description	Details
🔬 Scientific Regression	Symbolic regression on real scientific datasets	Scientific Regression Tutorial
💬 Prompt Engineering	Optimize LLM prompts for downstream tasks	Prompt Engineering Tutorial
🛡️ Adversarial Attacks	Evolve adversarial attack algorithms	Adversarial Attack Tutorial
⚡ CUDA Code Evolution	Evolve and optimize CUDA kernels	CUDA Task Tutorial

🚀 Quick Start¶

Installation¶

pip install evotoolkit

# Or install with all dependencies
pip install evotoolkit[all]

For detailed installation instructions, see the Installation Guide.

Your First Optimization¶

import evotoolkit
from evotoolkit.task.python_task.scientific_regression import ScientificRegressionTask
from evotoolkit.task.python_task import EvoEngineerPythonInterface
from evotoolkit.tools import HttpsApi

# 1. Create a task
task = ScientificRegressionTask(dataset_name="bactgrow")

# 2. Create an interface
interface = EvoEngineerPythonInterface(task)

# 3. Solve with LLM
llm_api = HttpsApi(
    api_url="https://api.openai.com/v1/chat/completions",
    key="your-api-key-here",
    model="gpt-4o"
)
result = evotoolkit.solve(
    interface=interface,
    output_path='./results',
    running_llm=llm_api,
    max_generations=5
)

That's it! EvoToolkit will use the LLM to evolve mathematical equations to fit your scientific data.

For a complete walkthrough, check out the Getting Started Guide.

📚 Available Algorithms¶

Algorithm	Description
EvoEngineer	Main LLM-driven evolutionary algorithm
FunSearch	Function search optimization method
EoH	Evolution of Heuristics

See the Tutorials section for more usage examples.

📖 Documentation¶

Installation: Installation instructions and setup
Getting Started: Quick start guide and basic usage
Tutorials: Step-by-step tutorials for common tasks
API Reference: Detailed API documentation
Development: Contributing guidelines and architecture

🔗 Links¶

GitHub: https://github.com/pgg3/evotoolkit
PyPI: https://pypi.org/project/evotoolkit/
Paper: arXiv (submitted)

📄 License¶

EvoToolkit is dual-licensed:

Academic & Open Source Use: Free for academic research, education, and open source projects. Citation required for academic publications.
Commercial Use: Requires a separate commercial license. Contact pguo6680@gmail.com for licensing.

See LICENSE for full terms.

🙏 Citation¶

If you use EvoToolkit in your research, please cite:

@article{guo2025evotoolkit,
  title={evotoolkit: A Unified LLM-Driven Evolutionary Framework for Generalized Solution Search},
  author={Guo, Ping and Zhang, Qingfu},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025},
  note={Submitted to arXiv}
}

💬 Getting Help¶

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: pguo6680@gmail.com