Applications¶
Up to now, this library PyPop7 has been used (at least) in the following papers:
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.
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.
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.
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.
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.
Till now, our library PyPop7 has been used (at least) in the following projects:
https://opof.kavrakilab.org/ (2023)
https://github.com/TUIlmenauAMS/BlackBoxOptimizerSPcomparison (2023)
Till now, our paper about this library PyPop7 has been cited (at least) in the following papers:
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.
For other introductions/coverage, refer to e.g.: