[31] Jingda Deng and Qingfu Zhang. Approximating hypervolume and hypervolume contributions using polar coordinate. IEEE Transactions on Evolutionary Computation, 23(5):913-918, 2019.
[32] Richard Zhang and Daniel Golovin. Random hypervolume scalarizations for provable multi-objective black box optimization. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 11096-11105. PMLR, 2020.
[33] Carlos M Fonseca and Peter J Fleming. On the performance assessment and comparison of stochastic multiobjective optimizers. In International conference on parallel problem solving from nature, pages 584-593. Springer, 1996.
[34] Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359-366, 1989.
[35] Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, and Yusuke Nojima. Use of two reference points in hypervolume-based evolutionary multiobjective optimization algorithms. In Parallel Problem Solving from Nature-PPSN XV: 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part I 15, pages 384–396. Springer, 2018.
[36] Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, and Yusuke Nojima. How to specify a reference point in hypervolume calculation for fair performance comparison. Evolutionary computation, 26(3):411-440, 2018.
[37] Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, and Qingfu Zhang. Pareto set learning for expensive multi-objective optimization. arXiv e-prints, pages arXiv-2210, 2022.
[38] David A Van Veldhuizen and Gary B Lamont. Multiobjective evolutionary algorithm test suites. In Proceedings of the 1999 ACM symposium on Applied computing, pages 351-357, 1999.
[39] Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
[40] Harvey J Greenberg and William P Pierskalla. A review of quasi-convex functions. Operations research, 19(7):1553-1570, 1971.
[41] Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, and Liwei Wang. The expressive power of neural networks: A view from the width. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 6231-6239, 2017.
[42] Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation, 8(2):173-195, 2000.
[43] Ryoji Tanabe and Hisao Ishibuchi. An easy-to-use real-world multi-objective optimization problem suite. Applied Soft Computing, 89:106078, 2020.
[44] Simone Parisi, Matteo Pirotta, and Jan Peters. Manifold-based multi-objective policy search with sample reuse. Neurocomputing, 263:3-14, 2017.
[45] Xiaoliang Ma, Qingfu Zhang, Guangdong Tian, Junshan Yang, and Zexuan Zhu. On tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 22(2):226-244, 2017.
[46] Jingzhao Zhang, Tianxing He, Suvrit Sra, and Ali Jadbabaie. Why gradient clipping accelerates training: A theoretical justification for adaptivity. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020.