Sponsor
This open-source Python library for large-scale black-box optimization (PyPop7) was supported by Shenzhen Fundamental Research Program under Grant No. JCYJ20200109141235597 (2000000 Yuan granted to Prof. Shi from Southern University of Science and Technology (SUSTech) @Shenzhen, China from 2021 to 2023), and actively developed by three of his group members (Qiqi Duan also in Harbin Institute of Technology, Chang Shao also in University of Technology Sydney, and Guochen Zhou also in Zhejiang University).
Now Yijun Yang from Tencent/University of Technology Sydney (UTS)/Beihang University helps to suggest papers about Bayesian Optimization (BO).
Now Qi Zhao from SUSTech helps to proofread the documents.
Now Haobin Yang from SUSTech helps to proofread and standardize the documents.
Now Zonghan He from SUSTech helps to test the installation process on Windows10 OS (before 2024).
Now Yajing Tan from SUSTech is added as one of core developers in order to increase new black-box optimizers.
Now Jian Zeng from Guangdong Police College/Harbin Institute of Technology helps to collect BBOs for data mining.
Now XiangLong Chen from SUSTech helps to proofread and standardize the documents.
Now Yuwei Huang from SUSTech is added as one of core developers in order to increase new black-box optimizers.
From 2024/5/1, Xingyu Zhou from SUSTech is added as one of core developers in order to code and check new black-box optimizers from the machine learning (ML) community.
From 2021 to 2023, Mingyang Feng from University of Birmingham (UoB)/University of Electronic Science and Technology of China helped to search a number of papers involved in this library and test some code on MacOS X.
From 2022 to 2023, Zhuowei Wang from UTS helped to test some code on Linux and proofread some documents.
In 2023, Minghan Zhang from University of Warwick/Imperial College London helped to search some papers involved in this library and test some code on MacOS X.
We also acknowledge the initial (2017) discussions with Hao Tong (when at SUSTech, now at UoB) and recent (2022) discussions wih Changwu Huang (at SUSTech).
Finally, we thank very much for all the following open-source code to make repeatability much easier (still updated):