Applications
Up to now, this open-source Python library PyPop7 has been used/cited (at least) in the following papers:
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]
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]
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]
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]
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]
4: Duan, Q., Shao, C., Zhou, G., 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]
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]
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, this research paper entered the nomination list of the Best Paper Award on PPSN-2022]
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]
Till now, our library PyPop7 has been used (at least) in the following open-source projects:
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)
9: https://github.com/BruthYU/BPT-VLM (2023)
8: https://github.com/opoframework/opof [online documentation: https://opof.kavrakilab.org/] (2023)
7: https://github.com/pyanno4rt/pyanno4rt [online documentation: https://pyanno4rt.readthedocs.io/en/latest/] (2023)
6: https://github.com/TUIlmenauAMS/BlackBoxOptimizerSPcomparison (2023)
4: https://github.com/jeancroy/RP-fit (2023)
2: https://github.com/Evolutionary-Intelligence/M-DES (2023)
1: https://github.com/Evolutionary-Intelligence/dpop7 (2023): A parallel/distributed extension to PyPop7 (now actively developed).
For other introductions/coverage to this open-source library PyPop7, refer to e.g.:
中文博客网站CSDN - PyPop7中文示例