| Pareto multi-task learning X Lin, HL Zhen, Z Li, QF Zhang, S Kwong Advances in neural information processing systems 32, 2019 | 298 | 2019 |
| A simple yet efficient evolution strategy for large-scale black-box optimization Z Li, Q Zhang IEEE Transactions on Evolutionary Computation 22 (5), 637-646, 2017 | 60 | 2017 |
| Evolution strategies for continuous optimization: A survey of the state-of-the-art Z Li, X Lin, Q Zhang, H Liu Swarm and Evolutionary Computation 56, 100694, 2020 | 59 | 2020 |
| Fast covariance matrix adaptation for large-scale black-box optimization Z Li, Q Zhang, X Lin, HL Zhen IEEE transactions on cybernetics 50 (5), 2073-2083, 2018 | 48 | 2018 |
| Cooperative coevolution with knowledge-based dynamic variable decomposition for bilevel multiobjective optimization X Cai, Q Sun, Z Li, Y Xiao, Y Mei, Q Zhang, X Li IEEE Transactions on Evolutionary Computation 26 (6), 1553-1565, 2022 | 18 | 2022 |
| An efficient rank-1 update for Cholesky CMA-ES using auxiliary evolution path Z Li, Q Zhang 2017 IEEE Congress on Evolutionary Computation (CEC), 913-920, 2017 | 18 | 2017 |
| A batched scalable multi-objective bayesian optimization algorithm X Lin, HL Zhen, Z Li, Q Zhang, S Kwong arXiv preprint arXiv:1811.01323, 2018 | 13 | 2018 |
| What does the evolution path learn in CMA-ES? Z Li, Q Zhang International Conference on Parallel Problem Solving from Nature, 751-760, 2016 | 9 | 2016 |
| A kernel-based indicator for multi/many-objective optimization X Cai, Y Xiao, Z Li, Q Sun, H Xu, M Li, H Ishibuchi IEEE Transactions on Evolutionary Computation 26 (4), 602-615, 2021 | 8 | 2021 |
| Variable metric evolution strategies by mutation matrix adaptation Z Li, Q Zhang Information Sciences 541, 136-151, 2020 | 6 | 2020 |
| Nonlinear collaborative scheme for deep neural networks HL Zhen, X Lin, AZ Tang, Z Li, Q Zhang, S Kwong arXiv preprint arXiv:1811.01316, 2018 | 6 | 2018 |
| SA-ES: Subspace activation evolution strategy for black-box adversarial attacks Z Li, H Cheng, X Cai, J Zhao, Q Zhang IEEE Transactions on Emerging Topics in Computational Intelligence, 2022 | 5 | 2022 |
| Integrating preference by means of desirability function with evolutionary multi-objective optimization Z Li, HL Liu Intelligent Automation & Soft Computing 21 (2), 197-209, 2015 | 5 | 2015 |
| Preference-based evolutionary multi-objective optimization Z Li, HL Liu 2012 Eighth International Conference on Computational Intelligence and …, 2012 | 5 | 2012 |
| Integrating preferred weights with decomposition based multi-objective evolutionary algorithm Z Li, HL Liu 2014 Tenth International Conference on Computational Intelligence and …, 2014 | 4 | 2014 |
| Noisy optimization by evolution strategies with online population size learning Z Li, S Zhang, X Cai, Q Zhang, X Zhu, Z Fan, X Jia IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (9), 5816-5828, 2021 | 3 | 2021 |
| An efficient elitist covariance matrix adaptation for continuous local search in high dimension Z Li, J Deng, W Gao, Q Zhang, HL Liu 2019 IEEE Congress on Evolutionary Computation (CEC), 936-943, 2019 | 3 | 2019 |
| A simple yet efficient rank one update for covariance matrix adaptation Z Li, Q Zhang arXiv preprint arXiv:1710.03996, 2017 | 3 | 2017 |
| Hyper-parameter optimization for deep learning by surrogate-based model with weighted distance exploration Z Li, CA Shoemaker 2021 IEEE Congress on Evolutionary Computation (CEC), 917-925, 2021 | 1 | 2021 |
| Decoupling of Direction and Length for Cumulative Step Size Adaptation S Zhang, Z Li, D Yang, S Wang, X Cai 2021 17th International Conference on Computational Intelligence and …, 2021 | | 2021 |
Qingfu ZhangChair Professor, FIEEE, City University of Hong KongVerified email at cityu.edu.hk
