A framework that leverages LLMs to automatically generate and optimize heuristic algorithms for solving combinatorial optimization problems.

This repository implements a dual-layer evolutionary framework that:

• Uses LLMs to generate heuristic algorithms automatically
• Employs evolutionary algorithms to optimize both prompts and generated algorithms
• Supports multiple classic combinatorial optimization problems

• IS: Independent Set Problem
• MIKS: Maximum Independent K-Set Problem
• MVC: Minimum Vertex Cover Problem
• SC: Set Cover Problem

• Online Bin Packing: Dynamic bin packing with online constraints
• Traveling Salesman Problem: Classic TSP variants

For MILP problems, datasets are avaliable here.

For online BP problems, the dataset is involved in the code.

For TSP problems, datasets are available here.

Install the required dependencies:

pip install -r requirements.txt

Replace your_llm_endpoint and your_api_key in the code with your actual LLM endpoint and API key.

# Run Minimum Vertex Cover problem
python src/MILP\ Problems/MVC_eoh_change_prompt_ACP.py

# Run Independent Set problem  
python src/MILP\ Problems/IS_eoh_change_prompt_ACP.py

# Run Set Cover problem
python src/MILP\ Problems/SC_eoh_change_prompt_ACP.py

Experimental results and performance comparisons are available in results/results.md.

This project is licensed under the MIT License - see the LICENSE file for details.

Contributions are welcome! Please feel free to submit issues and pull requests.