ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning

Here we provide sourcecodes of ConfigX, which has been recently accpeted by AAAI 2025 as an Oral paper.

Citation

The PDF version of the paper is available here. If you find our ConfigX useful, please cite it in your publications or projects.

@inproceedings{guo2025configx,
  title={ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning},
  author={Guo, Hongshu and Ma, Zeyuan and Chen, Jiacheng and Ma, Yining and Cao, Zhiguang and Zhang, Xinglin and Gong, Yue-Jiao},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}

Requirements

You can install all of dependencies of ConfigX via the command below.

pip install -r requirements.txt

Train

The training process can be easily activated via the command below.

python main.py

For more adjustable settings, please refer to main.py and config.py for details.

Recording results: Log files will be saved to ./logs, the file structure is as follow:

logs
|--run_name
   |--logging files
   |--...

The saved checkpoints will be saved to ./outputs, the file structure is as follow:

outputs
|--run_name
   |--epoch-0.pt
   |--epoch-1.pt
   |--...

Rollout

The rollout process can be easily activated via the command below.

python main.py --test --load_path [The checkpoint saving directory, default to be "./outputs"] --load_name [The run_name of the target ConfigX model] --load_epoch [The epoch of the model]

For example, for testing the model with run_name "20240704T221142" at the 50th epoch, the command is:

python main.py --test --load_name 20240704T221142 --load_epoch 50