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Zhenhua Li
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Cited by
Year
Pareto multi-task learning
X Lin, HL Zhen, Z Li, QF Zhang, S Kwong
Advances in neural information processing systems 32, 2019
3132019
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
632020
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
612017
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
472018
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
212022
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
182017
A batched scalable multi-objective bayesian optimization algorithm
X Lin, HL Zhen, Z Li, Q Zhang, S Kwong
arXiv preprint arXiv:1811.01323, 2018
132018
What does the evolution path learn in CMA-ES?
Z Li, Q Zhang
International Conference on Parallel Problem Solving from Nature, 751-760, 2016
112016
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
102021
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 7 (3 …, 2022
82022
Variable metric evolution strategies by mutation matrix adaptation
Z Li, Q Zhang
Information Sciences 541, 136-151, 2020
62020
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
62018
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
52015
Integrating preferred weights with decomposition based multi-objective evolutionary algorithm
Z Li, HL Liu
2014 Tenth International Conference on Computational Intelligence and …, 2014
52014
Preference-based evolutionary multi-objective optimization
Z Li, HL Liu
2012 Eighth International Conference on Computational Intelligence and …, 2012
52012
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
42021
A simple yet efficient rank one update for covariance matrix adaptation
Z Li, Q Zhang
arXiv preprint arXiv:1710.03996, 2017
42017
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
32019
A Two-phase Constrained Multi-Objective Evolutionary Algorithm Based on the Constrained Decomposition Approach
H Xu, X Cai, Z Li, Z Fan
2021 IEEE 7th International Conference on Cloud Computing and Intelligent …, 2021
12021
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
12021
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