Chao Qian (钱 超) 副教授，博导，国家优青，CCF-IEEE CS青年科学家 科技创新2030“新一代人工智能”重大项目青年科学家 Associate Professor School of Artificial Intelligence Nanjing University Office: Room A502, International College Area, Xianlin Campus Address:163 Xianlin Avenue, Nanjing, Jiangsu, China, 210023 Email: qianc at nju dot edu dot cn, qianc at lamda dot nju dot edu dot cn

Short Biography

I received my B.Sc. and Ph.D. degrees in the Department of Computer Science and Technology from Nanjing University in 2009 and 2015, respectively, under the supervision of Prof. Zhi-Hua Zhou. After finishing my PhD, I became an associate researcher in the School of Computer Science and Technology, University of Science and Technology of China, until 2019, when I returned to Nanjing University as an associate professor in the School of Artificial Intelligence. I visited the University of the Basque Country (Spain) and the University of Birmingham (UK). I am also a member of LAMDA Group.

Research Interest

My research interests include artificial intelligence, evolutionary computation and machine learning. Now I am working on

• Theoretical analysis of evolutionary algorithms
• Designing safe evolutionary algorithms, i.e., evolutionary algorithms with provable approximation guarantee
• Designing efficient black-box optimization algorithms, e.g., Bayesian optimization, evolutionary strategies, evolutionary gradient optimization, cooperative coevolution, and learning to optimize
• Evolutionary learning, particularly evolutionary reinforcement learning, deep learning, and ensemble learning
• Applications to solve real-world complex optimziation problems, e.g., in wireless network, electronic design automation, and geoscience

Recent News

• I have been invited to be a Program Co-Chair of the 22nd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2025)
• I have been invited to give an Early Career Spotlight Talk "Towards Theoretically Grounded Evolutionary Learning" at IJCAI 2022
• I have been invited to be an Associate Editor of IEEE Transactions on Evolutionary Computation
• I (together with Liang Feng and Carlos A. Coello Coello) organize a Special Issue of SCIENCE CHINA Information Sciences titled "Evolutionary Learning and Optimization" Submission Deadline: April 30, 2023
• I have given an invited talk "Why Do We Need Theoretical Research of Evolutionary Algorithms?" at ECOLE 2023 [Slides]
• I have given a tutorial "Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at IEEE CEC 2023
• I was invited to attend the Dagstuhl Seminar "Theory of Randomized Optimization Heuristics"

Publications

Book

1. Zhi-Hua Zhou, Yang Yu, and Chao Qian. Evolutionary Learning: Advances in Theories and Algorithms. Singapore: Springer, 2019. (ISBN 978-981-13-5955-2)
   
   
2. 周志华, 俞扬, 钱超 著. 演化学习: 理论与算法进展, 北京: 人民邮电出版社, 2021. (ISBN 978-7-115-55803-9)

Journal Article (* indicates that I am the corresponding author)

1. Chao Qian. Can Evolutionary Clustering Have Theoretical Guarantees?
   IEEE Transactions on Evolutionary Computation, in press. [PDF]
   
   
2. Yu-Ran Gu, Chao Bian, Miqing Li, and Chao Qian*. Subset Selection for Evolutionary Multi-Objective Optimization.
   IEEE Transactions on Evolutionary Computation, in press. [PDF]
   
   
3. Dan-Xuan Liu and Chao Qian*. Result Diversification with Negative Type Distances by Multi-objective Evolutionary Algorithms.
   Fundamental Research, in press. [PDF]
   
   
4. Yi-Xiao He, Yu-Chang Wu, Chao Qian, and Zhi-Hua Zhou. Margin Distribution and Structural Diversity Guided Ensemble Pruning.
   Machine Learning, in press.
   
   
5. Zeqiong Lv, Chao Qian, and Yanan Sun. Benchmarking Analysis of Evolutionary Neural Architecture Search.
   IEEE Transactions on Evolutionary Computation, in press.
   
   
6. Guiying Li, Peng Yang, Chao Qian, Richang Hong, and Ke Tang. Stage-wise Magnitude-based Pruning for Recurrent Neural Networks.
   IEEE Transactions on Neural Networks and Learning Systems, in press. [PDF]
   
   
7. Chao Qian, Dan-Xuan Liu, Chao Feng, and Ke Tang. Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences.
   Theoretical Computer Science, 2023, 943: 241-266. [Preprint PDF][PDF]
   
   
8. Chao Qian, Dan-Xuan Liu, and Zhi-Hua Zhou. Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees.
   Artificial Intelligence, 2022, 309: 103737. [Preprint PDF][PDF]
   
   
9. Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, and Chao Qian. ZOOpt: Toolbox for Derivative-Free Optimization.
   Science China: Information Sciences, 2022, 65: 207101. [PDF]
   
   
10. Chao Qian. Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions.
    Evolutionary Computation, 2021, 29(4): 463–490. [Preprint PDF][PDF]
    
    
11. Chao Qian, Chao Bian, Yang Yu, Ke Tang, and Xin Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails.
    Algorithmica, 2021, 83: 940-975. [Preprint PDF][PDF]
    
    
12. Wenjing Hong, Chao Qian*, and Ke Tang. Efficient Minimum Cost Seed Selection with Theoretical Guarantees for Competitive Influence Maximization.
    IEEE Transactions on Cybernetics, 2021, 51(12): 6091-6104. [Preprint PDF][PDF](code)
    
    
13. Chao Bian, Chao Qian*, Yang Yu, and Ke Tang. On the Robustness of Median Sampling in Noisy Evolutionary Optimization.
    Science China: Information Sciences, 2021, 64(5): 1-13. [Preprint PDF][PDF]
    
    
14. Chao Bian, Chao Qian*, Ke Tang, and Yang Yu. Running Time Analysis of the (1+1)-EA for Robust Linear Optimization.
    Theoretical Computer Science, 2020, 843: 57-72. [Preprint PDF][PDF]
    
    
15. Chao Qian. Distributed Pareto Optimization for Large-scale Noisy Subset Selection.
    IEEE Transactions on Evolutionary Computation, 2020, 24(4): 694-707. [Preprint PDF][PDF](code)
    
    
16. Chao Qian, Yang Yu, Ke Tang, Xin Yao, and Zhi-Hua Zhou. Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms.
    Artificial Intelligence, 2019, 275: 279-294. [Preprint PDF][PDF]
    
    
17. Chao Qian, Chao Bian, Wu Jiang, and Ke Tang. Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise.
    Algorithmica, 2019, 81: 749-795. [Preprint PDF][PDF]
    
    
18. Chao Qian, Jing-Cheng Shi, Ke Tang, and Zhi-Hua Zhou. Constrained Monotone k-Submodular Function Maximization Using Multi-objective Evolutionary Algorithms with Theoretical Guarantee.
    IEEE Transactions on Evolutionary Computation, 2018, 22(4): 595-608. [Preprint PDF] [Supplementary] [PDF](code)
    
    
19. Chao Qian, Yang Yu, Ke Tang, Yaochu Jin, Xin Yao, and Zhi-Hua Zhou. On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments.
    Evolutionary Computation, 2018, 26(2): 237-267. [Preprint PDF] [Supplementary][PDF]
    
    
20. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Analyzing Evolutionary Optimization in Noisy Environments.
    Evolutionary Computation, 2018, 26(1): 1-41. [Preprint PDF] [PDF]
    
    
21. Yang Yu, Chao Qian, and Zhi-Hua Zhou. Switch Analysis for Running Time Analysis of Evolutionary Algorithms.
    IEEE Transactions on Evolutionary Computation, 2015, 19(6): 777-792. [Preprint PDF] [PDF]
    
    
22. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Variable Solution Structure Can be Helpful in Evolutionary Optimization.
    Science China: Information Sciences, 2015, 58(11): 1-17. [Preprint PDF] [PDF]
    
    
23. Chao Qian, Yang Yu, and Zhi-Hua Zhou. An Analysis on Recombination in Multi-Objective Evolutionary Optimization.
    Artificial Intelligence, 2013, 204: 99-119. [Preprint PDF] [PDF]
    

Conference Paper (* indicates my student)

1. Ren-Jian Wang*, Ke Xue, Yutong Wang, Peng Yang, Haobo Fu, Qiang Fu, and Chao Qian. Diversity from Human Feedback.
   In: 2nd Agent Learning in Open-Endedness Workshop at NeurIPS'23, New Orleans, LA, 2023 . [PDF]
   
   
2. Yunqi Shi*, Ke Xue, Lei Song, and Chao Qian. Macro Placement by Wire-Mask-Guided Black-Box Optimization.
   In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA, 2023 . [PDF](code)
   
   
3. Chao Bian*, Yawen Zhou, Miqing Li, and Chao Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms.
   In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macao, SAR, China, 2023, pp.5513-5521. [PDF]
   
   
4. Ren-Jian Wang*, Ke Xue, Haopu Shang, Chao Qian, Haobo Fu, and Qiang Fu. Multi-objective Optimization-based Selection for Quality-Diversity by Non-surrounded-dominated Sorting.
   In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macao, SAR, China, 2023, pp.4335-4343. [PDF](code)
   
   
5. Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, and Yang Yu. Fast Teammate Adaptation in the Presence of Sudden Policy Change.
   In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI'23), Pittsburgh, PA, 2023, pp.2465-2476. [PDF]
   
   
6. Frank Neumann, Aneta Neumann, Chao Qian, Viet Anh Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, and Thomas Bäck. Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler.
   In: Proceedings of the 2023 IEEE Congress on Evolutionary Computation (CEC'23), Chicago, IL, 2023, pp.1-9. [PDF]
   
   
7. Shuang Wu, Jian Yao, Haobo Fu, Ye Tian, Chao Qian, Yaodong Yang, Qiang Fu, and Yang Wei. Quality-Similar Diversity via Population Based Reinforcement Learning.
   In: Proceedings of the 11th International Conference on Learning Representations (ICLR'23), Kigali, Rwanda, 2023. [PDF]
   
   
8. Dan-Xuan Liu*, Xin Mu, and Chao Qian. Human Assisted Learning by Evolutionary Multi-objective Optimization.
   In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023, pp.12453-12461. [PDF](code)
   
   
9. Yu-Ran Gu*, Chao Bian, and Chao Qian. Submodular Maximization Under the Intersection of Matroid and Knapsack Constraints.
   In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023, pp.3959-3967. [PDF](code)
   
   
10. Lei Yuan, Zi-Qian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Li-He Li, Chao Qian, and Yang Yu. Robust Multi-agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers.
    In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023, pp.11753-11762. [PDF]
    
    
11. Lei Song*, Ke Xue, Xiaobin Huang, and Chao Qian. Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization. (Spotlight)
    In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022. [PDF](code)
    
    
12. Ke Xue*, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, and Yang Yu. Multi-agent Dynamic Algorithm Configuration. (Spotlight)
    In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022. [PDF](code)
    
    
13. Ding-Ming Liu*, Haopu Shang, Wenjing Hong, and Chao Qian. Multi-Objective Evolutionary Instance Selection for Multi-Label Classification.
    In: Proceedings of the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI'22), Shanghai, China, 2022, pp.548-561. [PDF]
    
    
14. Chao Bian* and Chao Qian. Better Running Time of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) by Using Stochastic Tournament Selection.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, pp.428-441. [PDF]
    
    
15. Zi-An Zhang*, Chao Bian, and Chao Qian. Running Time Analysis of the (1+1)-EA using Surrogate Models on OneMax and LeadingOnes.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, pp.512-525. [PDF]
    
    
16. Yu-Chang Wu*, Yi-Xiao He, Chao Qian, and Zhi-Hua Zhou. Multi-objective Evolutionary Ensemble Pruning Guided by Margin Distribution.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, pp.427-441. [PDF](code)
    
    
17. Jia-Liang Wu*, Haopu Shang, Wenjing Hong, and Chao Qian. Robust Neural Network Pruning by Cooperative Coevolution.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, pp.459-473. [PDF](code)
    
    
18. Chao Qian. Towards Theoretically Grounded Evolutionary Learning.
    In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Vienna, Austria, 2022, pp.5826-5830. [PDF]
    (Early Career Paper)
    
    
19. Haopu Shang*, Jia-Liang Wu, Wenjing Hong, and Chao Qian. Neural Network Pruning by Cooperative Coevolution.
    In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Vienna, Austria, 2022, pp.4814-4820. [PDF](code)
    
    
20. Chao Bian*, Yawen Zhou, and Chao Qian. Robust Subset Selection by Greedy and Evolutionary Pareto Optimization.
    In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Vienna, Austria, 2022, pp.4726-4732. [PDF](code)
    
    
21. Yutong Wang*, Ke Xue, and Chao Qian. Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning.
    In: Proceedings of the 10th International Conference on Learning Representations (ICLR'22), Virtual, 2022. [PDF](code)
    
    
22. Ke Xue*, Chao Qian, Ling Xu, and Xudong Fei. Evolutionary Gradient Descent for Non-convex Optimization.
    In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual, 2021, pp.3221-3227. [PDF](code)
    
    
23. Chao Bian*, Chao Qian, Frank Neumann, and Yang Yu. Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints.
    In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual, 2021, pp.2191-2197. [PDF]
    
    
24. Fei-Yu Liu* and Chao Qian. Prediction Guided Meta-Learning for Multi-Objective Reinforcement Learning.
    In: Proceedings of the 2021 IEEE Congress on Evolutionary Computation (CEC'21), Krakow, Poland, 2021, pp.2171-2178. [PDF]
    (Best Student Paper Award Nomination)
    
    
25. Chao Feng* and Chao Qian. Multi-objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee.
    In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), Virtual, 2021, pp.12302-12310. [PDF](code)
    
    
26. Fei-Yu Liu*, Zi-Niu Li, and Chao Qian. Self-Guided Evolution Strategies with Historical Estimated Gradients.
    In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020, pp.1474-1480. [PDF](code)
    
    
27. Chao Qian, Hang Xiong, and Ke Xue. Bayesian Optimization using Pseudo-Points.
    In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020, pp.3044-3050. [PDF with Appendix]
    
    
28. Chao Qian, Chao Bian, and Chao Feng. Subset Selection by Pareto Optimization with Recombination.
    In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020, pp.2408-2415. [PDF with Appendix](code)
    
    
29. Chao Bian*, Chao Feng, Chao Qian, and Yang Yu. An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints.
    In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020, pp.3267-3274. [PDF](code)
    
    
30. Chao Feng*, Chao Qian, and Ke Tang. Unsupervised Feature Selection by Pareto Optimization.
    In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, pp.3534-3541. [PDF](code)
    
    
31. Chao Bian*, Chao Qian, and Ke Tang. Towards a Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under General Bit-wise Noise.
    In: Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN'18), Coimbra, Portugal, 2018, pp.165-177. [PDF]
    
    
32. Wu Jiang, Chao Qian, and Ke Tang. Improved Running Time Analysis of the (1+1)-ES on the Sphere Function.
    In: Proceedings of the 14th International Conference on Intelligent Computing (ICIC'18), Wuhan, China, 2018, pp.729-739. [PDF]
    
    
33. Mengxi Wu, Chao Qian, and Ke Tang. Dynamic Mutation Based Pareto Optimization for Subset Selection.
    In: Proceedings of the 14th International Conference on Intelligent Computing (ICIC'18), Wuhan, China, 2018, pp.25-35. [PDF]
    
    
34. Chao Bian*, Chao Qian, and Ke Tang. A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms.
    In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.1405-1411. [PDF]
    
    
35. Chao Qian, Yang Yu, and Ke Tang. Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection.
    In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.1478-1484. [PDF]
    
    
36. Chao Qian, Chao Feng, and Ke Tang. Sequence Selection by Pareto Optimization.
    In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.1485-1491. [PDF](code)
    
    
37. Chao Qian, Guiying Li, Chao Feng, and Ke Tang. Distributed Pareto Optimization for Subset Selection.
    In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.1492-1498. [PDF](code)
    
    
38. Guiying Li, Chao Qian, Chunhui Jiang, Xiaofen Lu, and Ke Tang. Optimization based Layer-wise Magnitude-based Pruning for DNN Compression.
    In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2383-2389. [PDF]
    
    
39. Chunhui Jiang, Guiying Li, Chao Qian, and Ke Tang. Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error.
    In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2298-2304. [PDF]
    
    
40. Chao Qian, Chao Bian, Yang Yu, Ke Tang, and Xin Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails.
    In: Proceedings of the 20th ACM Conference on Genetic and Evolutionary Computation (GECCO'18), Kyoto, Japan, 2018, pp.1507-1514. [PDF with Appendix]
    
    
41. Chao Qian, Yibo Zhang, Ke Tang, and Xin Yao. On Multiset Selection with Size Constraints.
    In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.1395-1402. [PDF] [Supplementary](code)
    
    
42. Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Subset Selection under Noise.
    In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, 2017, pp.3563-3573. [PDF] [Supplementary](code)
    
    
43. Chunhui Jiang, Guiying Li, and Chao Qian. Dynamic and Adaptive Threshold for DNN Compression from Scratch.
    In: Proceedings of the 11th International Conference on Simulated Evolution and Learning (SEAL'17), Shenzhen, China, 2017, pp.858-869. [PDF]
    
    
44. Chao Qian, Jing-Cheng Shi, Yang Yu, and Ke Tang. On Subset Selection with General Cost Constraints.
    In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2613-2619. [PDF](code)
    
    
45. Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Optimizing Ratio of Monotone Set Functions.
    In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2606-2612. [PDF](code)
    
    
46. Chao Qian, Chao Bian, Wu Jiang, and Ke Tang. Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise.
    In: Proceedings of the 19th ACM Conference on Genetic and Evolutionary Computation (GECCO'17), Berlin, Germany, 2017, pp.1399-1406. [PDF]
    
    
47. Jing-Cheng Shi, Chao Qian, and Yang Yu. Evolutionary Multi-objective Optimization Made Faster by Sequential Decomposition.
    In: Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC'17), San Sebastian, Spain, 2017, pp.2488-2493. [PDF]
    
    
48. Chao Qian, Yang Yu, and Zhi-Hua Zhou. A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions.
    In: Proceedings of the 17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'16), Yangzhou, China, 2016, pp.457-467. [PDF]
    (Best Paper Award)
    
    
49. Chao Qian, Ke Tang, and Zhi-Hua Zhou. Selection Hyper-heuristics Can Provably be Helpful in Evolutionary Multi-objective Optimization.
    In: Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN'16), Edinburgh, Scotland, 2016, pp.835-846. [PDF]
    
    
50. Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Parallel Pareto Optimization for Subset Selection.
    In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1939-1945. [PDF](code)
    
    
51. Bingdong Li, Chao Qian, Jinlong Li, Ke Tang, and Xin Yao. Search Based Recommender System Using Many-Objective Evolutionary Algorithm.
    In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC'16), Vancouver, Canada, 2016, pp.120-126. [PDF]
    
    
52. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Subset Selection by Pareto Optimization.
    In: Advances in Neural Information Processing Systems 28 (NIPS'15), Montreal, Canada, 2015, pp.1765-1773. [PDF](code)
    
    
53. Chao Qian, Yang Yu, and Zhi-Hua Zhou. On Constrained Boolean Pareto Optimization.
    In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.389-395. [PDF]
    
    
54. Yang Yu and Chao Qian. Running Time Analysis: Convergence-based Analysis Reduces to Switch Analysis.
    In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC'15), Sendai, Japan, 2015, pp.2603-2610. [PDF]
    
    
55. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Pareto Ensemble Pruning.
    In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.2935-2941. [PDF] [Long Version](code)
    
    
56. Chao Qian, Yang Yu, Yaochu Jin, and Zhi-Hua Zhou. On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments.
    In: Proceedings of the 13th International Conference on Parallel Problem Solving from Nature (PPSN'14), Ljubljana, Slovenia, 2014, pp.302-311. [PDF]
    
    
57. Chao Qian, Yang Yu, and Zhi-Hua Zhou. On Algorithm-Dependent Boundary Case Identification for Problem Classes.
    In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN'12), Taormina, Italy, 2012, pp.62-71. [PDF]
    
    
58. Chao Qian, Yang Yu, and Zhi-Hua Zhou. An Analysis on Recombination in Multi-Objective Evolutionary Optimization.
    In: Proceedings of the 13th ACM Conference on Genetic and Evolutionary Computation (GECCO'11), Dublin, Ireland, 2011, pp.2051-2058. [PDF]
    (Best Theory Paper Award)
    
    
59. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Collisions are Helpful for Computing Unique Input-Output Sequences.
    In: Proceedings of the 13th ACM Conference on Genetic and Evolutionary Computation (GECCO'11), Dublin, Ireland, 2011, pp.265-266. [PDF] (poster)
    
    
60. Yang Yu, Chao Qian, and Zhi-Hua Zhou. Towards Analyzing Recombination Operators in Evolutionary Search.
    In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN'10), Krakow, Poland, 2010, pp.144-153. [PDF]

Technical Report (* indicates my student)

1. Yibo Zhang*, Chao Qian, and Ke Tang. Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization.
   CORR abs/1810.06833, 2018. [PDF]
   

Native Paper

1. 钱超. 基于演化学习的子集选择研究进展. 中国人工智能学会通讯, 2020, 10(5): 15-21. [PDF]
   
   
2. 俞扬, 钱超. 演化学习专刊前言. 软件学报, 2018, 29(9). [PDF]
   
   
3. 钱超. 多目标演化学习理论与方法研究. 中国人工智能学会通讯, 2017, 7(9): 20-29. [PDF]
   
   
4. 钱超, 周志华. 基于分解策略的多目标演化子集选择算法. 中国科学: 信息科学, 2016, 46(9): 1276-1287. [PDF]
   
   
5. 钱超, 俞扬. 演化学习研究进展. 中国人工智能学会通讯, 2016, 6(8): 7-12. [PDF]
   
   
6. 钱超, 俞扬. 机器学习顶级会议NIPS 2015. 中国计算机学会通讯, 2016, 12(6): 80-82. [PDF]

Awards

• CCF-IEEE CS Young Computer Scientist Award (CCF青年科技奖/CCF-IEEE CS青年科学家奖), 2023
• Huawei Spark Award (华为“难题揭榜”火花奖，EDA专题难题：超高维空间多目标黑盒优化技术), 2023
• Young Teacher Prize of Nanjing University "Advanced Teacher Morality"(南京大学“师德先进”青年教师奖), 2023
• Heuristic Search and Evolutionary Algorithms voted as "The Good Course in My Mind"(我心目中的好课程) by graduates, 2023
• National Key R&D Program of China, CNY 5,000,000, PI: Chao Qian (科技创新2030“新一代人工智能”重大项目青年科学家), 2022
• IJCAI 2022 Early Career Spotlight Talk
• Huawei Spark Award (华为“难题揭榜”火花奖，ADN自动驾驶网络难题：大规模复杂网络中多参数耦合、多目标竞争下快速寻优), 2022
• May 4th Youth Medal of Nanjing University (南京大学青年五四奖章), 2022
• IEEE CEC 2021 Best Student Paper Award Nomination (with Fei-Yu Liu)
• National Science Foundation for Excellent Young Scholars (国家优秀青年科学基金), 2020
• Outstanding Young Associate Editor of Frontiers of Computer Science, 2019-2020
• Young Elite Scientists Sponsorship Program by CAST (中国科协青年人才托举工程), 2016-2018
• Outstanding Doctoral Dissertation Award of CAAI (中国人工智能学会优博), 2017
• Excellence Award of CCF-Tencent Open Research Fund, 2016
• IDEAL 2016 Best Paper Award (with Yang Yu and Zhi-Hua Zhou)
• IBM PhD Fellowship Award, 2014
• PAKDD 2012 Data Mining Competition Grand Prize Winner (with Nan Li, Shao-Yuan Li, Yue Zhu, Qing Da and Zhi-Hua Zhou)
• ACM GECCO 2011 Best Theory Paper Award (with Yang Yu and Zhi-Hua Zhou)

Students

PhD Students

• 2020 - : Chao Bian 卞超 (考博, 获徐新奖学金, CCF-A类论文6篇, CCF-B类论文5篇)
• 2019 - : Ke Xue 薛轲 (硕博连读, 获国家奖学金, CCF-A类论文6篇，ICLR 1篇)
• 2020 - : Dan-Xuan Liu 刘丹璇 (硕博连读, CCF-A类论文2篇, CCF-B类论文1篇)
• 2022 - : Cheng-Rui Gao 高成睿 (直博)
• 2023 - : Haopu Shang 尚昊璞 (考博)

Master Students

• 2016 - 2019: Chao Bian 卞超 (获国家奖学金, CCF-A类论文1篇, CCF-B类论文2篇)
• 2017 - 2019: Chao Feng 冯超 (获国家奖学金, CCF-A类论文2篇, 快手), Hang Xiong 熊航 (CCF-A类论文1篇, PhD candidate at USTC)
• 2018 - 2021: Fei-Yu Liu 刘飞宇 (IEEE CEC 2021 Best Student Paper Award Nomination, CCF-A类论文1篇, 腾讯)
• 2019 - 2022: Haopu Shang 尚昊璞 (CCF-A类论文1篇, CCF-B类论文1篇)
• 2020 - 2023: Zihang Chen 程子航 (阿里巴巴), Dingming Liu 刘鼎铭 (华为), Yutong Wang 王雨桐 (ICLR 1篇, 华为), Jia-Liang Wu 吴嘉梁 (CCF-A类论文1篇, CCF-B类论文1篇, 字节跳动)
• 2021 - : Yuran Gu 辜宇然 (CCF-A类论文1篇, CCF-B类论文1篇), Yu-Chang Wu 吴宇畅 (CCF-B类论文1篇), Zi-An Zhang 张子安 (CCF-B类论文1篇), Yuheng Zhou 周煜恒
• 2022 - : Xiao-Bin Huang 黄晓斌 (CCF-A类论文1篇), Er-Long Liu 刘二龙, Lei Song 宋磊 (获国家奖学金, CCF-A类论文1篇), Ren-Jian Wang 王任柬 (CCF-A类论文1篇), Yawen Zhou 周雅雯 (CCF-A类论文1篇)
• 2023 - : Xiao He 何潇, Tianhao Lu 陆天浩, Ziyu Nie 聂梓宇, Zhijia Qiu 仇志佳, Yunqi Shi 侍昀琦 (CCF-A类论文1篇), Shukuan Wang 汪舒宽

Undergraduate Students

• 2021 - : Haotian Chai 柴浩天, Chen Lu 卢晨, Zhuoxia Wang 王卓夏, Yuheng Wu 吴羽珩, Rongxi Tan 谭荣熙, Yuji Zhang 张运吉
• 2020 - : Shengjie Ren 任圣杰, Yiheng Xu 徐一珩
• 2019 - 2023: Kaiwen Li 李楷文, Zhijia Qiu 仇志佳, Yunqi Shi 侍昀琦 (美国大学生数学建模竞赛F奖), Shukuan Wang 汪舒宽, Mengchen Zhang 章梦晨
• 2018 - 2022: Junyu Sun 孙浚予, Yawen Zhou 周雅雯 (CCF-A类论文1篇), Lei Song 宋磊
• 2015 - 2019: Yibo Zhang 张益博 (获郭沫若奖学金, CCF-A类论文1篇, Now: PhD candidate at Stanford University)
• 2014 - 2018: Tianhao Wang 王天浩 (Now: PhD candidate at Yale University), Zhongjun Zhang 张仲骏 (Now: Alibaba)
• 2012 - 2016: Zhendong Lei 雷震东 (Now: PhD candidate at Chinese Academy of Sciences)

Visiting Students

• 2023.07 - 2023.09: Zeqiong Lv 吕泽琼, Sichuan University
• 2023.09 - : Liang Zhao 赵亮, City University of Hong Kong

Professional Activities

• Organization Services:

Chair of IEEE Computational Intelligence Society (CIS) Task Force on Theoretical Foundations of Bio-inspired Computation     2018 - 2021
Founding Chair of IEEE Computational Intelligence Society (CIS) Task Force on Evolutionary Learning     2023 -
Vice Chair of IEEE Computational Intelligence Society (CIS) Task Force on Theoretical Foundations of Bio-inspired Computation     2022 -
Vice Chair of IEEE CIS Students Activities Committee    2021 -
Member of Evolutionary Computation Technical Committee     2021 -
IEEE Senior Member

• Journal Associate/Guest Editors:

Associate Editor of IEEE Transactions on Evolutionary Computation     2022 -
Editorial Board Member of Memetic Computing     2021 -
Editorial Board Member of Communications of The CCF     2021 -
Young Associate Editor of Science China Information Sciences     2023 -
Young Associate Editor of Journal of Computer Science and Technology     2021 -
Young Associate Editor of Frontiers of Computer Science     2019 -
Guest Co-Editor (with Aneta Neumann and Per Kristian Lehre) of a Special Issue of Theoretical Computer Science entitled "Theoretical Foundations of Evolutionary Computation"     2023
Guest Co-Editor (with Bo Yang and ‪Changshui Zhang‬) of a Special Issue of Journal of Computer Research and Development (计算机研究与发展 数据挖掘前沿进展专刊)     2022
Guest Co-Editor (with Yang Yu) of a Special Issue of Journal of Software entitled "Evolutionary Learning" (软件学报 演化学习专刊)     2018

• Conference Program Chairs:

20th China Symposium on Machine Learning and Applications (MLA 2022) (together with Sheng-Jun Huang)

• Symposium Chairs:

"IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI)" at SSCI 2023 (together with Domingo Lopez and Leonardo Franco)
"IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI)" at SSCI 2022 (together with Manuel Ojeda-Aciego and Leonardo Franco)
"IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI)" at SSCI 2021 (together with Pietro S. Oliveto, Manuel Ojeda-Aciego, and Leonardo Franco)
"IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI)" at SSCI 2020 (together with Pietro S. Oliveto, Manuel Ojeda-Aciego, and Leonardo Franco)


• Special Session Chairs:

"Theoretical Foundations of Bio-inspired Computation" at WCCI 2022 (together with Per Kristian Lehre and Aneta Neumann)
"Theoretical Foundations of Bio-inspired Computation" at CEC 2021 (together with Per Kristian Lehre and Aneta Neumann)
"Theoretical Foundations of Bio-inspired Computation" at WCCI 2020 (together with Per Kristian Lehre and Aneta Neumann)
"Theory of Bio-Inspired Computation" at CEC 2019 (together with Mojgan Pourhassan and Frank Neumann)
"Evolutionary Optimization for Non-Convex Machine Learning" at WCCI 2018 (together with Ke Tang and Yang Yu)

• Competition Organizers:

"Evolutionary Submodular Optimisation" at GECCO 2023 (together with Aneta Neumann, Frank Neumann, Hao Wang, Saba Sadeghi Ahouei, and Jacob de Nobel)
"Evolutionary Submodular Optimisation" at GECCO 2022 (together with Aneta Neumann and Frank Neumann)

• Tutorial Speakers:

"Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at CEC 2023
"Evolutionary Submodular Optimisation" at GECCO 2022 (together with Aneta Neumann and Frank Neumann)
"Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at WCCI 2022
"Evolutionary Submodular Optimisation" at GECCO 2021 (together with Aneta Neumann and Frank Neumann)
"Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at CEC 2021 (together with Yang Yu)
"Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at PPSN 2020 [Slides] (together with Yang Yu)
"Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at WCCI 2020 [Slides] (together with Yang Yu)
"Pareto Optimization for Subset Selection: Theories and Practical Algorithms" at CEC 2019 [Slides] (together with Yang Yu)
"Pareto Optimization for Subset Selection: Theory and Applications in Machine Learning " at WCCI 2018 [Slides] (together with Yang Yu)
"An Introduction on Theory of Evolutionary Optimization" at ECOLE 2016 (第三届演化计算与学习研讨会) [Slides]

• Invited Talks:

"Towards Safe, Efficient and Universal Black-Box Optimization" at Huawei, 2023 (华为自动驾驶网络•百家讲坛)
"Why Do We Need Theoretical Research of Evolutionary Algorithms?" at ECOLE 2023 (第九届演化计算与学习研讨会) [Slides]
"Multi-objective Evolutionary Learning: Advances in Theories and Algorithms" at CCDM 2022 (第九届中国数据挖掘会议) [Slides]
"Multi-objective Evolutionary Learning: Advances in Theories and Algorithms" at ECOLE 2022 (第八届演化计算与学习研讨会)
"Multi-objective Evolutionary Learning: Advances in Theories and Algorithms" at MLA 2021 (第十九届中国机器学习及其应用研讨会)
"Result Diversification by Multi-objective Evolutionary Algorithms" at ThRaSH 2021 (Theory of Randomized Search Heuristics Workshop)
"Towards Safe Evolutionary Optimization" at JSAI 2020 (2020年江苏省人工智能学术会议) [Slides]
"Multi-objective Evolutionary Learning: Advances in Theories and Algorithms" at ECOLE 2019 (第六届演化计算与学习研讨会) [Slides]
"Subset Selection by Pareto Optimization: Theories and Practical Algorithms" at ECOLE 2018 (第五届演化计算与学习研讨会) [Slides]

• Journal Reviewers:

Algorithmica
Annals of Operations Research
Applied Soft Computing
Artificial Intelligence
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Knowledge Discovery from Data
Evolutionary Computation
Expert Systems with Applications
IEEE Computational Intelligence Magazine
IEEE Transactions on Affective Computing
IEEE Transactions on Cognitive and Developmental Systems
IEEE Transactions on Computational Social Systems
IEEE Transactions on Cybernetics
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Emerging Topics in Computational Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences
Information and Computation
INFORMS Journal on Computing
International Journal of Computer Mathematics
Journal of Machine Learning Research
Knowledge-Based Systems
Machine Learning
Mathematics and Computers in Simulation
Neurocomputing
Neural Networks
Pattern Recognition Letters
Swarm and Evolutionary Computation
SCIENCE CHINA Information Sciences
Theoretical Computer Science
Transactions on Machine Learning Research
中国科学
软件学报
计算机学报
自动化学报

• Conference PC Members:

  2024: GECCO (Theory Track), IJCAI (Senior PC)
  2023: ECAI (Senior PC), FOGA, GECCO (Theory Track), ICLR, IJCAI, NeurIPS
  2022: AAAI, AISTATS, AutoML, GECCO (Theory Track), ICML, IJCAI (Senior PC), NeurIPS, PPSN
  2021: AAAI, AISTATS, CICAI (Area Chair), FOGA, GECCO (Theory Track), ICML, IJCAI (Senior PC), NeurIPS
  2020: ACML, AAAI, AISTATS, ECAI (Senior PC), GECCO (Theory Track), ICML, IJCAI (Senior PC), LOD, PPSN
  2019: ACML, AAAI, AISTATS, FOGA, GECCO (Theory Track), ICML, IJCAI, IJCNN, KDD, LOD, NeurIPS, PAKDD, PRICAI, SSCI
  2018: ACML, AAAI, AISTATS, GECCO (Theory Track), ICML, IJCAI, IJCNN, LOD, NeurIPS, PAKDD, PRICAI, SSCI
  2017: ACML, AAAI, AISTATS, CEC, GECCO (Theory Track), IJCAI (Senior PC), IJCNN, MOD, NIPS, SDM, SEAL, SSCI
  2016: ACML, CEC, GECCO (Theory Track), ICPR, IJCAI, MOD, NIPS, PRICAI, SSCI
  2015: IJCAI, SSCI

Teaching

• Stochastic Processes (for undergraduate students, Fall, 2021; Spring, 2022/2023)
• Heuristic Search and Evolutionary Algorithms (for undergraduate and graduate students, Fall, 2020/2021/2022/2023)