LLM4AD

Introduction

LLM4AD is an open-source Python-based Platform leveraging Large Language Models (LLMs) for Automatic Algorithm Design (AD). Please refer to the paper [LLM4AD] for detailed information, including the overview, methodology, and benchmark results.

LLM4AD is accomplished with Documents and Examples materials to support users and developers to easily test, build and deploy their own LLM4AD applications and conduct research.

LLM4AD was originally developed for optimisation tasks. The framework is versatile enough to be used in other areas, including machine learning, science discovery, game theory and engineering design.

For more information, see the contact list

News

• 2026.02 New Champion! Our LLM4AD system won the CVRPLib BKS competition and established 51 new Best Known Solutions on large-scale CVRP benchmarks.

• 2026.01 Our Survey Paper "A Systematic Survey on Large Language Models for Algorithm Design" has been accepted by ACM Computing Surveys ! A Rep for the Survey can be found here

• 2025.6 We're excited to share that EoH recently set a New World Record in Circle Packing Problem, achieving a score of 2.63594 for 26 circles ! Results here

• 2025.6 LLM4AD at CEC 2025 Tutorial !

• 2025.3 LLM4AD at EMO 2025 Tutorial !

• 2024.12 LLM4AD paper Released “LLM4AD: A Platform for Algorithm Design with Large Language Model" !

• 2024.11 LLM4AD v1.0 Released !

• 2024.10 Survey Paper “A Systematic Survey on Large Language Models for Algorithm Design” is online !

Features of our package

Requirements & Installation

[!Important] The Python version MUST be larger or equal to Python 3.9, and less than Python 3.13.

[!Important] If you are testing machine learning tasks or using GUI, please install gym via pip install gym. Please note that the gym version may be conflict with your own Python environment, please refer to gym's docs to obtain appropriate version.

• Please refer to requirements.txt

• Please install numba (if you want to use Numba accelerate)

• Please install tensorboard (if you want to use a Tensorboard logger)

• Please install wandb (if you want to use wandb logger)

• Please install gym (if you want to try GUI, and Machine Learning tasks)

• Please install pandas (if you want to try Science Discovery tasks)

• Please install all required packages in requirements.txt (if you want to use GUI)

Install LLM4AD locally

We suggest to install and run LLM4AD in conda env with python>=3.9, =3.9, [!Note]

Configure your LLM api before running the script. For example:

1. Set host: 'api.deepseek.com'
2. Set key: 'your api key'
3. Set model: 'deepseek-chat'

…

More Examples:

• Constructive Heuristics for TSP
• Constructive Heuristics for VRPTW
• ...

Check Documents for more tasks and examples

GUI usage:

[!Important] Install all required packages in requirements.txt for GUI usage.

$ cd GUI
$ python run_gui.py

Check GUI Introduction for more information

LLM4AD Search Methods

⚙️ LLM4AD Algorithm Design Tasks