Efficient Guided Evolution for Neural Architecture Search


Create a conda environment using the env.yml file

conda env create -f env.yml

Activate the environment and follow the instructions to install

conda activate gea

Install nasbench (see https://github.com/google-research/nasbench)

Download the NDS data from https://github.com/facebookresearch/nds and place the json files in path_to_code/nds_data/
Download the NASbench101 data (see https://github.com/google-research/nasbench)
Download the NASbench201 data (see https://github.com/D-X-Y/NAS-Bench-201)

Reproduce all of the results by running


The code is licensed under the MIT licence.


This repository makes liberal use of code from the AutoDL library, NAS-Bench-201, NAS-Bench-101 and NAS-WOT. We are grateful to the authors for making the implementations publicly available.

Citing us

If you use or build on our work, please consider citing us:

    title={Guided Evolution for Neural Architecture Search},
    author={Vasco Lopes and Miguel Santos and Bruno Degardin and Luís A. Alexandre},
    booktitle={Advances in Neural Information Processing Systems 35 (NeurIPS) - New In ML}


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