| Data-driven evolutionary optimization: An overview and case studies Y Jin, H Wang, T Chugh, D Guo, K Miettinen IEEE Transactions on Evolutionary Computation 23 (3), 442-458, 2018 | 577 | 2018 |
| Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization H Wang, L Jiao, X Yao IEEE Transactions on Evolutionary Computation 19 (4), 524--541, 2015 | 559 | 2015 |
| A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization L Pan, C He, Y Tian, H Wang, X Zhang, Y Jin IEEE Transactions on Evolutionary Computation 23 (1), 74-88, 2018 | 384 | 2018 |
| Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems H Wang, Y Jin, J Doherty IEEE transactions on cybernetics 47 (9), 2664-2677, 2017 | 377 | 2017 |
| Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor Y Sun, H Wang, B Xue, Y Jin, GG Yen, M Zhang IEEE Transactions on Evolutionary Computation 24 (2), 350-364, 2019 | 274 | 2019 |
| Diversity assessment in many-objective optimization H Wang, Y Jin, X Yao IEEE transactions on cybernetics 47 (6), 1510-1522, 2016 | 265 | 2016 |
| Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system H Wang, Y Jin, JO Jansen IEEE Transactions on Evolutionary Computation 20 (6), 939-952, 2016 | 251 | 2016 |
| Offline data-driven evolutionary optimization using selective surrogate ensembles H Wang, Y Jin, C Sun, J Doherty IEEE Transactions on Evolutionary Computation 23 (2), 203-216, 2018 | 211 | 2018 |
| A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems H Wang, Y Jin IEEE transactions on cybernetics 50 (2), 536-549, 2018 | 179 | 2018 |
| A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization Z Song, H Wang, C He, Y Jin IEEE Transactions on Evolutionary Computation 25 (6), 1013-1027, 2021 | 173 | 2021 |
| A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges H Wang, M Olhofer, Y Jin Complex & Intelligent Systems 3, 233-245, 2017 | 156 | 2017 |
| Corner Sort for Pareto-Based Many-Objective Optimization H Wang, X Yao IEEE Transactions on Cybernetics 44 (1), 92-102, 2014 | 147 | 2014 |
| Effectiveness and efficiency of non-dominated sorting for evolutionary multi-and many-objective optimization Y Tian, H Wang, X Zhang, Y Jin Complex & Intelligent Systems 3, 247-263, 2017 | 141 | 2017 |
| Objective reduction based on nonlinear correlation information entropy H Wang, X Yao Soft Computing 20, 2393-2407, 2016 | 85 | 2016 |
| A repository of real-world datasets for data-driven evolutionary multiobjective optimization C He, Y Tian, H Wang, Y Jin Complex & Intelligent Systems 6 (1), 189-197, 2020 | 80 | 2020 |
| Data-driven evolutionary optimization Y Jin, H Wang, C Sun Springer International Publishing, 2021 | 77 | 2021 |
| A surrogate-assisted evolutionary feature selection algorithm with parallel random grouping for high-dimensional classification S Liu, H Wang, W Peng, W Yao IEEE Transactions on Evolutionary Computation 26 (5), 1087-1101, 2022 | 67 | 2022 |
| A generic test suite for evolutionary multifidelity optimization H Wang, Y Jin, J Doherty IEEE Transactions on Evolutionary Computation 22 (6), 836-850, 2017 | 64 | 2017 |
| A co-evolutionary multi-objective optimization algorithm based on direction vectors LC Jiao, H Wang, RH Shang, F Liu Information Sciences 228, 90-112, 2013 | 63 | 2013 |
| Surrogate-assisted evolutionary multitasking for expensive minimax optimization in multiple scenarios H Wang, L Feng, Y Jin, J Doherty IEEE Computational Intelligence Magazine 16 (1), 34-48, 2021 | 59 | 2021 |
Yaochu JinChair Professor for AI, MAE FIEEE, Westlake University, China在 westlake.edu.cn 的电子邮件经过验证
