Author:
Yao Yinghua,Pan Yuangang,Li Jing,Tsang Ivor,Yao Xin
Funder
A*STAR Centre for Frontier AI Research
Program for Guangdong Provincial Key Laboratory
Publisher
Springer Science and Business Media LLC
Reference62 articles.
1. Afshari, H., Hare, W., & Tesfamariam, S. (2019). Constrained multi-objective optimization algorithms: Review and comparison with application in reinforced concrete structures. Applied Soft Computing, 83, 105631. https://doi.org/10.1016/J.ASOC.2019.105631
2. Andrieu, C., De Freitas, N., Doucet, A., et al. (2003). An introduction to MCMC for machine learning. Machine Learning, 50, 5–43. https://doi.org/10.1023/A:1020281327116
3. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In International conference on machine learning (pp. 214–223). https://proceedings.mlr.press/v70/arjovsky17a.html
4. Borghi, G., Herty, M., & Pareschi, L. (2023). An adaptive consensus based method for multi-objective optimization with uniform pareto front approximation. Applied Mathematics and Optimization, 88(2), 58. https://doi.org/10.1007/s00245-023-10036-y
5. Cheng, R., Li, M., Tian, Y., et al. (2017). A benchmark test suite for evolutionary many-objective optimization. Complex and Intelligent Systems, 3, 67–81. https://doi.org/10.1007/s40747-017-0039-7