Applications
@misc{2026-arXiv-Qiu, author={Wenjie Qiu and Zixin Wang and Hongyu Fang and Zeyuan Ma and Yue-Jiao Gong}, title={A learning-based cooperative coevolution framework for heterogeneous large-scale global optimization}, eprint={2604.01241}, archivePrefix={arXiv}, primaryClass={cs.NE}, url={https://arxiv.org/abs/2604.01241}, year={2026}, }
Applications&Citations
Up to now, this open-source Python library PyPop7 has been used and/or cited (at least) in the following papers (note that the below list is actively updated):
[cited] indicates that PyPop7 has been cited in any location by the corresponding paper.
[used] indicates that PyPop7 has been used by the corresponding paper.
15: Fiks, I.S. and Fiks, G.E., 2024. Determining the minimum number of compensating monopole sources required to suppress the integral radiation level. Acoustical Physics, 70(5), pp.914-918.
cited
Russian: https://bioethicsjournal.ru/0320-7919/article/view/648447
14: Santana, R., Inza, I., Prol-Godoy, I. and Picallo-Perez, A., 2024, November. Continuous estimation of distribution algorithms for the parametric optimization of geothermal power plants. In Proceedings of International Conference on Computational Intelligence and Intelligent Systems (pp. 86-93). ACM.
cited
used
13: Miranda, P.B., Giráldez-Cru, J., Silva-Filho, M.W., Zarco, C. and Cordón, O., 2024, June. Learning agents’ behavioral patterns in agent-Based modeling by means of evolutionary algorithms. In IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE.
cited (but NOT used) in its code project
12: Ma, Z., Chen, J., Guo, H. and Gong, Y.J., 2024. Neural Exploratory Landscape Analysis. arXiv preprint arXiv:2408.10672. [used&cited]
South China University of Technology
11: Pinchuk, M., Kirgizov, G., Yamshchikova, L., Nikitin, N., Deeva, I., Shakhkyan, K., Borisov, I., Zharkov, K. and Kalyuzhnaya, A., 2024, July. GOLEM: Flexible Evolutionary Design of Graph Representations of Physical and Digital Objects. In Proceedings of Annual Genetic and Evolutionary Computation Conference Companion (pp. 1668-1675). ACM. [cited]
ITMO University
10: Vodopija, A., Cork, J.N. and Filipič, B., 2024, July. The Lunar Lander Landing Site Selection Benchmark Reexamined: Problem Characterization and Algorithm Performance. In Proceedings of Annual Genetic and Evolutionary Computation Conference (pp. 1381-1389). ACM. [used&cited]
Jozef Stefan Institute + Jozef Stefan International Postgraduate School
JADE
9: Bailo, R., Barbaro, A., Gomes, S.N., Riedl, K., Roith, T., Totzeck, C. and Vaes, U., 2024. CBX: Python and Julia Packages for Consensus-based Interacting Particle Methods. arXiv preprint arXiv:2403.14470. [cited]
University of Oxford + Technische Universiteit Delft + University of Warwick + Technical University of Munich + Munich Center for Machine Learning + Deutsches Elektronen-Synchrotron DESY + University of Wuppertal + Inria + Ecole des Ponts
8: Ma, Z., Guo, H., Chen, J., Peng, G., Cao, Z., Ma, Y. and Gong, Y.J., 2024. LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation. arXiv preprint arXiv:2403.01131. [used&cited]
South China University of Technology + Singapore Management University + Nanyang Technological University
7: Zhang, Z., Wei, Y. and Sui, Y., 2024. An Invariant Information Geometric Method for High-Dimensional Online Optimization. arXiv preprint arXiv:2401.01579. [used&cited]
Tsinghua University
6: Yu, L., Chen, Q., Lin, J. and He, L., 2023. Black-box Prompt Tuning for Vision-Language Model as a Service. Proceedings of International Joint Conference on Artificial Intelligence (pp. 1686-1694). IJCAI. [used]
East China Normal University
5: Lee, Y., Lee, K., Hsu, D., Cai, P. and Kavraki, L.E., 2023. The Planner Optimization Problem: Formulations and Frameworks. arXiv preprint arXiv:2303.06768. [used&cited]
Rice University + Shanghai Jiao Tong University + National University of Singapore
4: Duan, Q., Shao, C., Zhou, G., Zhang, M., Zhao, Q. and Shi, Y., 2023. Distributed Evolution Strategies with Multi-Level Learning for Large-Scale Black-Box Optimization. arXiv preprint arXiv:2310.05377. [used]
Duan, Q., Shao, C., Zhou, G., Zhang, M., Zhao, Q. and Shi, Y., 2024. Distributed Evolution Strategies With Multi-Level Learning for Large-Scale Black-Box Optimization. IEEE Transactions on Parallel & Distributed Systems, 35(11), pp.2087-2101.
Harbin Institute of Technology + Southern University of Science and Technology + University of Technology Sydney + University of Warwick
3: Duan, Q., Shao, C., Zhou, G., Yang, H., Zhao, Q. and Shi, Y., 2023. Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations. arXiv preprint arXiv:2304.05020. [used]
Harbin Institute of Technology + Southern University of Science and Technology + University of Technology Sydney
2: Duan, Q., Zhou, G., Shao, C., Yang, Y. and Shi, Y., 2022. Collective Learning of Low-Memory Matrix Adaptation for Large-Scale Black-Box Optimization. In International Conference on Parallel Problem Solving from Nature (pp. 281-294). Springer, Cham.
used (heavily depend upon PyPop7)
this paper entered the nomination list of the Best Paper Award on PPSN-2022
https://github.com/Evolutionary-Intelligence/D-LM-MA has been not maintained now since its more advanced versions are provided
Harbin Institute of Technology + Southern University of Science and Technology + University of Technology Sydney
1: Duan, Q., Zhou, G., Shao, C., Yang, Y. and Shi, Y., 2022, July. Distributed Evolution Strategies for Large-Scale Optimization. In Proceedings of ACM Genetic and Evolutionary Computation Conference Companion (pp. 395-398). ACM.
used (heavily depend upon PyPop7)
https://github.com/Evolutionary-Intelligence/DES has been deleted since its more advanced versions are provided
Harbin Institute of Technology + Southern University of Science and Technology + University of Technology Sydney
Open-Source Cases
Till now, this Python library PyPop7 has been required and/or introduced (at least) in the following open-source projects on GitHub:
24: https://github.com/Wukong-SCUT/HCC
from pypop7.optimizers.es.mmes import MMES
from pypop7.optimizers.es.cmaes import CMAES
23: https://github.com/Witcape/PSO
from pypop7.optimizers.pso.pso import PSO
22: https://github.com/LijunSun90/Knowledge_with_Codes
from pypop7.optimizers.pso.spso import SPSO as PSO
21: https://github.com/XAI-liacs/BLADE
pyproject.toml: pypop7 = “^0.0.79”
20: https://github.com/LOG-postech/ZIP
requirements.txt: pypop7
19: https://github.com/yangyongkang2000/SEvoBench
from pypop7.optimizers.de.shade import SHADE
from pypop7.benchmarks.base_functions import rosenbrock
18: https://github.com/GMC-DRL/Awesome-MetaBBO
MetaBox + LLM4Opt + pypop7 + EvoX + evosax + …
“Many outstanding teams have developed excellent GitHub repositories for the Evolutionary Computation community, and we are pleased to share them here.”
17: https://github.com/lamda-bbo/BBOPlace-Bench
from pypop7.optimizers.pso.pso import PSO as PYPSO
requirements.txt: pypop7==0.0.82
16: https://github.com/lamda-bbo/BBOPlace-miniBench
from pypop7.optimizers.pso.pso import PSO as PYPSO
requirements.txt: pypop7==0.0.82
15: https://github.com/GMC-DRL/Neur-ELA
requirements.txt: pypop7==0.0.79
from pypop7.optimizers.es import FCMAES, SEPCMAES, RMES, CMAES
14: https://github.com/nikivanstein/LLaMEA
requirements.txt: pypop7 = “^0.0.79”
13: https://github.com/AmitDIRTYC0W/neuronveil-mnist-train
pyproject.toml: “pypop7 ~= 0.0.79”
from pypop7.optimizers.pso.clpso import CLPSO
from pypop7.optimizers.ga.gl25 import GL25
from pypop7.optimizers.de.shade import SHADE
from pypop7.optimizers.de.jade import JADE
from pypop7.optimizers.ep.lep import LEP
11: https://github.com/aiboxlab/evolutionary-abm-calibration (2024)
10: https://github.com/Echozqn/llm [https://github.com/Echozqn/llm/tree/main/collie/examples/alpaca/eda] (2024)
Unfortunately, this open-source project is not openly accessible now.
9: https://github.com/BruthYU/BPT-VLM (2023)
8: https://github.com/opoframework/opof [online docs: https://opof.kavrakilab.org/] (2023)
7: https://github.com/pyanno4rt/pyanno4rt [online docs: https://pyanno4rt.readthedocs.io/en/latest/] (2023)
Tim Ortkamp: Scientific Computing Center, Karlsruhe Institute of Technology (KIT) + Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ) + Helmholtz Information and Data Science School for Health
LMCMA + LMMAES
6: https://github.com/TUIlmenauAMS/BlackBoxOptimizerSPcomparison (2023)
4: https://github.com/jeancroy/RP-fit (2023)
3: https://github.com/moesio-f/py-abm-public (2023)
Unfortunately, this open-source project is not openly accessible now.
2: https://github.com/Evolutionary-Intelligence/M-DES (2023)
1: https://github.com/Evolutionary-Intelligence/dpop7 (2023)
This is a parallel/distributed extension to PyPop7 (now actively developed).
Introduction&Involvement
For introduction / coverage / involvement to this library PyPop7, please refer to e.g.:
Online Praises
All of the following praises come from online states. We appreciate very much for these unstinting praises, given that we do not have an interest relationship with all of them: