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ORCIDChao Qian 0001
钱 超
Person information
- unicode name: 钱 超
- affiliation: Nanjing University, State Key Laboratory for Novel Software Technology, China
- affiliation (former): University of Science and Technology of China, School of Computer Science and Technology, Hefei, China
- affiliation: University of Science and Technology of China, School of Computer Science and Technology, Hefei, China
- affiliation (PhD 2015): Nanjing University, State Key Laboratory for Novel Software Technology, China
Other persons with the same name
- Chao Qian 0002 — Ningbo University, Ningbo, Zhejiang, China
- Chao Qian 0003 — Southeast University, National Mobile Communications Research Laboratory, Nanjing, China
- Chao Qian 0004 — Brown University, Providence, RI, USA
- Chao Qian 0005 — Columbia University, New York, NY, USA
- Chao Qian 0006 — Chang'an University, Xi'an, China
- Chao Qian 0007 — Huazhong University of Science and Technology, Wuhan, China
- Chao Qian 0008 — Tsinghua University, Division of Intelligent and Bio-mimetic Machinery, State Key Laboratory of Tribology, Beijing, China
- Chao Qian 0009
![[0000-0003-1706-2008]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-1706-2008")
— University of Duisburg-Essen, Duisburg, Germany - Chao Qian 0010 — Hong Kong University of Science and Technology, Department of Mechanical and Aerospace Engineering, Hong Kong
- Chao Qian 0011 — Lamar University, Department of Industrial Engineering, Beaumont, TX, USA
- Chao Qian 0012 — Zhejiang University, College of Information Science and Electronic Engineering, Hangzhou, China
- Chao Qian 0013 — Hunan University, College of Computer Science and Electronic Engineering, China
- Chao Qian 0014 — Soochow University, School of Electronic and Information Engineering, Suzhou, China
- Chao Qian 0015 — Beijing Institute of Technology, Department of Mechatronical Engineering, China
- Chao Qian 0016 — Zhejiang University, College of Chemical and Biological Engineering, Hangzhou, China
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2020 – today
- 2024
[j22]Yi-Xiao He, Dan-Xuan Liu, Shen-Huan Lyu, Chao Qian, Zhi-Hua Zhou:
Multi-class imbalance problem: A multi-objective solution. Inf. Sci. 680: 121156 (2024)- [j21]Yi-Xiao He, Yu-Chang Wu, Chao Qian, Zhi-Hua Zhou:
Margin distribution and structural diversity guided ensemble pruning. Mach. Learn. 113(6): 3545-3567 (2024) - [j20]Yu-Ran Gu, Chao Bian, Miqing Li, Chao Qian:
Subset Selection for Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Comput. 28(2): 403-417 (2024) - [j19]Guiying Li, Peng Yang, Chao Qian, Richang Hong, Ke Tang:
Stage-Wise Magnitude-Based Pruning for Recurrent Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35(2): 1666-1680 (2024) - [c65]Xiaobin Huang, Lei Song, Ke Xue, Chao Qian:
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation. AAAI 2024: 12635-12643 - [c64]Tianhao Lu, Chao Bian, Chao Qian:
Towards Running Time Analysis of Interactive Multi-Objective Evolutionary Algorithms. AAAI 2024: 20777-20785 - [c63]Zeqiong Lv, Chao Bian, Chao Qian, Yanan Sun:
Runtime Analysis of Population-based Evolutionary Neural Architecture Search for a Binary Classification Problem. GECCO 2024 - [c62]Ke Xue, Ren-Jian Wang, Pengyi Li, Dong Li, Jianye Hao, Chao Qian:
Sample-Efficient Quality-Diversity by Cooperative Coevolution. ICLR 2024 - [i47]Chao Qian, Ke Xue, Ren-Jian Wang:
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization. CoRR abs/2401.10539 (2024) - [i46]Zeqiong Lv, Chao Qian, Yanan Sun:
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search. CoRR abs/2401.11712 (2024) - [i45]Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao, Zongzhang Zhang, Chao Qian:
Reinforced In-Context Black-Box Optimization. CoRR abs/2402.17423 (2024) - [i44]Ke Xue, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, Chao Qian:
Escaping Local Optima in Global Placement. CoRR abs/2402.18311 (2024) - [i43]Chao Bian, Shengjie Ren, Miqing Li, Chao Qian:
An Archive Can Bring Provable Speed-ups in Multi-Objective Evolutionary Algorithms. CoRR abs/2406.02118 (2024) - [i42]Shengjie Ren, Zhijia Qiu, Chao Bian, Miqing Li, Chao Qian:
Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization. CoRR abs/2406.02658 (2024) - [i41]Ke Xue, Rong-Xi Tan, Xiaobin Huang, Chao Qian:
Offline Multi-Objective Optimization. CoRR abs/2406.03722 (2024) - [i40]Ren-Jian Wang, Ke Xue, Cong Guan, Chao Qian:
Quality-Diversity with Limited Resources. CoRR abs/2406.03731 (2024) - [i39]Yu-Chang Wu, Shen-Huan Lyu, Haopu Shang, Xiangyu Wang, Chao Qian:
Confidence-aware Contrastive Learning for Selective Classification. CoRR abs/2406.04745 (2024) - [i38]Dan-Xuan Liu, Yi-Heng Xu, Chao Qian:
Peptide Vaccine Design by Evolutionary Multi-Objective Optimization. CoRR abs/2406.05743 (2024) - [i37]Zi-Hang Cheng, Haopu Shang, Chao Qian:
Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection. CoRR abs/2406.09180 (2024) - [i36]Dan-Xuan Liu, Chao Qian:
Biased Pareto Optimization for Subset Selection with Dynamic Cost Constraints. CoRR abs/2406.12383 (2024) - [i35]Shengjie Ren, Chao Bian, Miqing Li, Chao Qian:
A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2). CoRR abs/2406.16116 (2024) - 2023
- [j18]Chao Qian, Dan-Xuan Liu, Chao Feng, Ke Tang:
Multi-objective evolutionary algorithms are generally good: Maximizing monotone submodular functions over sequences. Theor. Comput. Sci. 943: 241-266 (2023) - [j17]Per Kristian Lehre, Aneta Neumann, Chao Qian:
Special Issue on Theoretical Foundations of Evolutionary Computation. Theor. Comput. Sci. 950: 113785 (2023) - [c61]Yu-Ran Gu, Chao Bian, Chao Qian:
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints. AAAI 2023: 3959-3967 - [c60]Lei Yuan, Ziqian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Lihe Li, Chao Qian, Yang Yu:
Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers. AAAI 2023: 11753-11762 - [c59]Dan-Xuan Liu, Xin Mu, Chao Qian:
Human Assisted Learning by Evolutionary Multi-Objective Optimization. AAAI 2023: 12453-12461 - [c58]Frank Neumann, Aneta Neumann, Chao Qian, Anh Viet Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, Thomas Bäck:
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. CEC 2023: 1-9 - [c57]Shuang Wu, Jian Yao, Haobo Fu, Ye Tian, Chao Qian, Yaodong Yang, Qiang Fu, Wei Yang:
Quality-Similar Diversity via Population Based Reinforcement Learning. ICLR 2023 - [c56]Ren-Jian Wang, Ke Xue, Haopu Shang, Chao Qian, Haobo Fu, Qiang Fu:
Multi-objective Optimization-based Selection for Quality-Diversity by Non-surrounded-dominated Sorting. IJCAI 2023: 4335-4343 - [c55]Chao Bian, Yawen Zhou, Miqing Li, Chao Qian:
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. IJCAI 2023: 5513-5521 - [c54]Yunqi Shi, Ke Xue, Song Lei, Chao Qian:
Macro Placement by Wire-Mask-Guided Black-Box Optimization. NeurIPS 2023 - [c53]Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, Yang Yu:
Fast Teammate Adaptation in the Presence of Sudden Policy Change. UAI 2023: 2465-2476 - [i34]Frank Neumann, Aneta Neumann, Chao Qian, Anh Viet Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, Thomas Bäck:
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. CoRR abs/2302.01464 (2023) - [i33]Lei Yuan, Ziqian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Lihe Li, Chao Qian, Yang Yu:
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers. CoRR abs/2305.05909 (2023) - [i32]Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, Yang Yu:
Fast Teammate Adaptation in the Presence of Sudden Policy Change. CoRR abs/2305.05911 (2023) - [i31]Chao Bian, Yawen Zhou, Miqing Li, Chao Qian:
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. CoRR abs/2306.02611 (2023) - [i30]Yunqi Shi, Ke Xue, Lei Song, Chao Qian:
Macro Placement by Wire-Mask-Guided Black-Box Optimization. CoRR abs/2306.16844 (2023) - [i29]Yu-Ran Gu, Chao Bian, Chao Qian:
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints. CoRR abs/2307.09487 (2023) - [i28]Chengrui Gao, Haopu Shang, Ke Xue, Dong Li, Chao Qian:
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy. CoRR abs/2308.14104 (2023) - [i27]Ren-Jian Wang, Ke Xue, Yutong Wang, Peng Yang, Haobo Fu, Qiang Fu, Chao Qian:
Diversity from Human Feedback. CoRR abs/2310.06648 (2023) - [i26]Tianhao Lu, Chao Bian, Chao Qian:
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms. CoRR abs/2310.08384 (2023) - [i25]Dan-Xuan Liu, Yu-Ran Gu, Chao Qian, Xin Mu, Ke Tang:
Migrant Resettlement by Evolutionary Multi-objective Optimization. CoRR abs/2310.08896 (2023) - [i24]Xiaobin Huang, Lei Song, Ke Xue, Chao Qian:
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation. CoRR abs/2312.10423 (2023) - 2022
- [j16]Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou:
Result diversification by multi-objective evolutionary algorithms with theoretical guarantees. Artif. Intell. 309: 103737 (2022) - [j15]Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Chao Qian, Yang Yu:
ZOOpt: a toolbox for derivative-free optimization. Sci. China Inf. Sci. 65(10) (2022) - [c52]Aneta Neumann, Frank Neumann, Chao Qian:
Evolutionary submodular optimisation: tutorial. GECCO Companion 2022: 1427-1449 - [c51]Yutong Wang, Ke Xue, Chao Qian:
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning. ICLR 2022 - [c50]Chao Bian, Yawen Zhou, Chao Qian:
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization. IJCAI 2022: 4726-4732 - [c49]Haopu Shang, Jia-Liang Wu, Wenjing Hong, Chao Qian:
Neural Network Pruning by Cooperative Coevolution. IJCAI 2022: 4814-4820 - [c48]Chao Qian:
Towards Theoretically Grounded Evolutionary Learning. IJCAI 2022: 5826-5830 - [c47]Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu:
Multi-agent Dynamic Algorithm Configuration. NeurIPS 2022 - [c46]Lei Song, Ke Xue, Xiaobin Huang, Chao Qian:
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization. NeurIPS 2022 - [c45]Yu-Chang Wu, Yi-Xiao He, Chao Qian, Zhi-Hua Zhou:
Multi-objective Evolutionary Ensemble Pruning Guided by Margin Distribution. PPSN (1) 2022: 427-441 - [c44]Chao Bian, Chao Qian:
Better Running Time of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) by Using Stochastic Tournament Selection. PPSN (2) 2022: 428-441 - [c43]Jia-Liang Wu, Haopu Shang, Wenjing Hong, Chao Qian:
Robust Neural Network Pruning by Cooperative Coevolution. PPSN (1) 2022: 459-473 - [c42]Zi-An Zhang, Chao Bian, Chao Qian:
Running Time Analysis of the (1+1)-EA Using Surrogate Models on OneMax and LeadingOnes. PPSN (2) 2022: 512-525 - [c41]Dingming Liu, Haopu Shang, Wenjing Hong, Chao Qian:
Multi-objective Evolutionary Instance Selection for Multi-label Classification. PRICAI (1) 2022: 548-561 - [i23]Chao Bian, Chao Qian:
Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection. CoRR abs/2203.11550 (2022) - [i22]Haopu Shang, Jia-Liang Wu, Wenjing Hong, Chao Qian:
Neural Network Pruning by Cooperative Coevolution. CoRR abs/2204.05639 (2022) - [i21]Chao Bian, Yawen Zhou, Chao Qian:
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization. CoRR abs/2205.01415 (2022) - [i20]Ke Xue, Yutong Wang, Lei Yuan, Cong Guan, Chao Qian, Yang Yu:
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution. CoRR abs/2208.04957 (2022) - [i19]Lei Song, Ke Xue, Xiaobin Huang, Chao Qian:
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization. CoRR abs/2210.01628 (2022) - [i18]Zeqiong Lv, Chao Qian, Gary G. Yen, Yanan Sun:
Analysis of Expected Hitting Time for Designing Evolutionary Neural Architecture Search Algorithms. CoRR abs/2210.05397 (2022) - [i17]Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu:
Multi-agent Dynamic Algorithm Configuration. CoRR abs/2210.06835 (2022) - [i16]Chao Qian:
Can Evolutionary Clustering Have Theoretical Guarantees? CoRR abs/2212.01771 (2022) - 2021
- [j14]Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao:
Analysis of Noisy Evolutionary Optimization When Sampling Fails. Algorithmica 83(4): 940-975 (2021) - [j13]Chao Bian, Chao Qian, Yang Yu, Ke Tang:
On the robustness of median sampling in noisy evolutionary optimization. Sci. China Inf. Sci. 64(5) (2021) - [j12]Chao Qian:
Multiobjective Evolutionary Algorithms Are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions. Evol. Comput. 29(4): 463-490 (2021) - [j11]Wenjing Hong, Chao Qian, Ke Tang:
Efficient Minimum Cost Seed Selection With Theoretical Guarantees for Competitive Influence Maximization. IEEE Trans. Cybern. 51(12): 6091-6104 (2021) - [c40]Chao Feng, Chao Qian:
Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee. AAAI 2021: 12302-12310 - [c39]Fei-Yu Liu, Chao Qian:
Prediction Guided Meta-Learning for Multi-Objective Reinforcement Learning. CEC 2021: 2171-2178 - [c38]Aneta Neumann, Frank Neumann, Chao Qian:
Evolutionary submodular optimisation. GECCO Companion 2021: 918-940 - [c37]Chao Bian, Chao Qian, Frank Neumann, Yang Yu:
Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints. IJCAI 2021: 2191-2197 - [c36]Ke Xue, Chao Qian, Ling Xu, Xudong Fei:
Evolutionary Gradient Descent for Non-convex Optimization. IJCAI 2021: 3221-3227 - [i15]Chao Qian, Dan-Xuan Liu, Chao Feng, Ke Tang:
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences. CoRR abs/2104.09884 (2021) - [i14]Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou:
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees. CoRR abs/2110.09332 (2021) - [i13]Shengcai Liu, Ning Lu, Cheng Chen, Chao Qian, Ke Tang:
HydraText: Multi-objective Optimization for Adversarial Textual Attack. CoRR abs/2111.01528 (2021) - 2020
- [j10]Chao Bian, Chao Qian, Ke Tang, Yang Yu:
Running time analysis of the (1+1)-EA for robust linear optimization. Theor. Comput. Sci. 843: 57-72 (2020) - [j9]Chao Qian:
Distributed Pareto Optimization for Large-Scale Noisy Subset Selection. IEEE Trans. Evol. Comput. 24(4): 694-707 (2020) - [c35]Chao Qian, Chao Bian, Chao Feng:
Subset Selection by Pareto Optimization with Recombination. AAAI 2020: 2408-2415 - [c34]Chao Bian, Chao Feng, Chao Qian, Yang Yu:
An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints. AAAI 2020: 3267-3274 - [c33]Fei-Yu Liu, Zi-Niu Li, Chao Qian:
Self-Guided Evolution Strategies with Historical Estimated Gradients. IJCAI 2020: 1474-1480 - [c32]Chao Qian, Hang Xiong, Ke Xue:
Bayesian Optimization using Pseudo-Points. IJCAI 2020: 3044-3050
2010 – 2019
- 2019
- [b1]Zhi-Hua Zhou, Yang Yu, Chao Qian:
Evolutionary Learning: Advances in Theories and Algorithms. Springer 2019, ISBN 978-981-13-5955-2, pp. 3-293 - [j8]Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou:
Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms. Artif. Intell. 275: 279-294 (2019) - [j7]Chao Qian, Chao Bian, Wu Jiang, Ke Tang:
Running Time Analysis of the ( $$1+1$$ 1 + 1 )-EA for OneMax and LeadingOnes Under Bit-Wise Noise. Algorithmica 81(2): 749-795 (2019) - [c31]Chao Feng, Chao Qian, Ke Tang:
Unsupervised Feature Selection by Pareto Optimization. AAAI 2019: 3534-3541 - [i12]Chao Bian, Chao Qian, Ke Tang:
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization. CoRR abs/1906.06873 (2019) - [i11]Chao Bian, Chao Qian, Yang Yu:
On the Robustness of Median Sampling in Noisy Evolutionary Optimization. CoRR abs/1907.13100 (2019) - [i10]Chao Qian, Hang Xiong:
Bayesian Optimization using Pseudo-Points. CoRR abs/1910.05484 (2019) - [i9]Chao Qian:
Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions. CoRR abs/1910.05492 (2019) - 2018
- [j6]Chao Qian, Yang Yu, Zhi-Hua Zhou:
Analyzing Evolutionary Optimization in Noisy Environments. Evol. Comput. 26(1) (2018) - [j5]Chao Qian, Yang Yu, Ke Tang, Yaochu Jin, Xin Yao, Zhi-Hua Zhou:
On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments. Evol. Comput. 26(2) (2018) - [j4]Chao Qian, Jing-Cheng Shi, Ke Tang, Zhi-Hua Zhou:
Constrained Monotone k-Submodular Function Maximization Using Multiobjective Evolutionary Algorithms With Theoretical Guarantee. IEEE Trans. Evol. Comput. 22(4): 595-608 (2018) - [c30]Chao Qian, Yibo Zhang, Ke Tang, Xin Yao:
On Multiset Selection With Size Constraints. AAAI 2018: 1395-1402 - [c29]Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao:
Analysis of noisy evolutionary optimization when sampling fails. GECCO 2018: 1507-1514 - [c28]Mengxi Wu, Chao Qian, Ke Tang:
Dynamic Mutation Based Pareto Optimization for Subset Selection. ICIC (3) 2018: 25-35 - [c27]Wu Jiang, Chao Qian, Ke Tang:
Improved Running Time Analysis of the (1+1)-ES on the Sphere Function. ICIC (1) 2018: 729-739 - [c26]Chao Bian, Chao Qian, Ke Tang:
A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms. IJCAI 2018: 1405-1411 - [c25]Chao Qian, Yang Yu, Ke Tang:
Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection. IJCAI 2018: 1478-1484 - [c24]Chao Qian, Chao Feng, Ke Tang:
Sequence Selection by Pareto Optimization. IJCAI 2018: 1485-1491 - [c23]Chao Qian, Guiying Li, Chao Feng, Ke Tang:
Distributed Pareto Optimization for Subset Selection. IJCAI 2018: 1492-1498 - [c22]Chunhui Jiang, Guiying Li, Chao Qian, Ke Tang:
Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error. IJCAI 2018: 2298-2304 - [c21]Guiying Li, Chao Qian, Chunhui Jiang, Xiaofen Lu, Ke Tang:
Optimization based Layer-wise Magnitude-based Pruning for DNN Compression. IJCAI 2018: 2383-2389 - [c20]Chao Bian, Chao Qian, Ke Tang:
Towards a Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes Under General Bit-Wise Noise. PPSN (2) 2018: 165-177 - [i8]Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, Chao Qian:
ZOOpt/ZOOjl: Toolbox for Derivative-Free Optimization. CoRR abs/1801.00329 (2018) - [i7]Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao:
Analysis of Noisy Evolutionary Optimization When Sampling Fails. CoRR abs/1810.05045 (2018) - [i6]Yibo Zhang, Chao Qian, Ke Tang:
Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization. CoRR abs/1810.06833 (2018) - 2017
- [c19]Jing-Cheng Shi, Chao Qian, Yang Yu:
Evolutionary multi-objective optimization made faster by sequential decomposition. CEC 2017: 2488-2493 - [c18]Chao Qian, Chao Bian, Wu Jiang, Ke Tang:
Running time analysis of the (1+1)-EA for onemax and leadingones under bit-wise noise. GECCO 2017: 1399-1406 - [c17]Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou:
Optimizing Ratio of Monotone Set Functions. IJCAI 2017: 2606-2612 - [c16]Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang:
On Subset Selection with General Cost Constraints. IJCAI 2017: 2613-2619 - [c15]Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou:
Subset Selection under Noise. NIPS 2017: 3560-3570 - [c14]Chunhui Jiang, Guiying Li, Chao Qian:
Dynamic and Adaptive Threshold for DNN Compression from Scratch. SEAL 2017: 858-869 - [i5]Chao Qian, Chao Bian, Wu Jiang, Ke Tang:
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise. CoRR abs/1711.00956 (2017) - [i4]Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou:
Maximizing Non-monotone/Non-submodular Functions by Multi-objective Evolutionary Algorithms. CoRR abs/1711.07214 (2017) - 2016
- [c13]Bingdong Li, Chao Qian, Jinlong Li, Ke Tang, Xin Yao:
Search based recommender system using many-objective evolutionary algorithm. CEC 2016: 120-126 - [c12]Chao Qian, Yang Yu, Zhi-Hua Zhou:
A Lower Bound Analysis of Population-Based Evolutionary Algorithms for Pseudo-Boolean Functions. IDEAL 2016: 457-467 - [c11]Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou:
Parallel Pareto Optimization for Subset Selection. IJCAI 2016: 1939-1945 - [c10]Chao Qian, Ke Tang, Zhi-Hua Zhou:
Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective Optimization. PPSN 2016: 835-846 - [i3]Chao Qian, Yang Yu, Zhi-Hua Zhou:
A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions. CoRR abs/1606.03326 (2016) - 2015
- [j3]Chao Qian, Yang Yu, Zhi-Hua Zhou:
Variable solution structure can be helpful in evolutionary optimization. Sci. China Inf. Sci. 58(11): 1-17 (2015) - [j2]Yang Yu, Chao Qian, Zhi-Hua Zhou:
Switch Analysis for Running Time Analysis of Evolutionary Algorithms. IEEE Trans. Evol. Comput. 19(6): 777-792 (2015) - [c9]Chao Qian, Yang Yu, Zhi-Hua Zhou:
Pareto Ensemble Pruning. AAAI 2015: 2935-2941 - [c8]Yang Yu, Chao Qian:
Running time analysis: Convergence-based analysis reduces to switch analysis. CEC 2015: 2603-2610 - [c7]Chao Qian, Yang Yu, Zhi-Hua Zhou:
On Constrained Boolean Pareto Optimization. IJCAI 2015: 389-395 - [c6]Chao Qian, Yang Yu, Zhi-Hua Zhou:
Subset Selection by Pareto Optimization. NIPS 2015: 1774-1782 - 2014
- [c5]Chao Qian, Yang Yu, Yaochu Jin, Zhi-Hua Zhou:
On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments. PPSN 2014: 302-311 - 2013
- [j1]Chao Qian, Yang Yu, Zhi-Hua Zhou:
An analysis on recombination in multi-objective evolutionary optimization. Artif. Intell. 204: 99-119 (2013) - [i2]Chao Qian, Yang Yu, Zhi-Hua Zhou:
Analyzing Evolutionary Optimization in Noisy Environments. CoRR abs/1311.4987 (2013) - 2012
- [c4]Chao Qian, Yang Yu, Zhi-Hua Zhou:
On Algorithm-Dependent Boundary Case Identification for Problem Classes. PPSN (1) 2012: 62-71 - 2011
- [c3]Chao Qian, Yang Yu, Zhi-Hua Zhou:
Collisions are helpful for computing unique input-output sequences. GECCO (Companion) 2011: 265-266 - [c2]Chao Qian, Yang Yu, Zhi-Hua Zhou:
An analysis on recombination in multi-objective evolutionary optimization. GECCO 2011: 2051-2058 - [i1]Yang Yu, Chao Qian, Zhi-Hua Zhou:
Towards Analyzing Crossover Operators in Evolutionary Search via General Markov Chain Switching Theorem. CoRR abs/1111.0907 (2011) - 2010
- [c1]Yang Yu, Chao Qian, Zhi-Hua Zhou:
Towards Analyzing Recombination Operators in Evolutionary Search. PPSN (1) 2010: 144-153
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-31 21:40 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
