Chao Bian @ LAMDA, NJU-AI

bianc.jpg 
Chao Bian
Ph.D. candidate, LAMDA Group
School of Artificial Intelligence
National Key Laboratory for Novel Software Technology
Nanjing University, Nanjing 210023, China

Supervisor: Professor Yang Yu, Associate Professor Chao Qian

Email: bianc@lamda.nju.edu.cn
Office: Room A516, YiFu Building, Xianlin Campus of Nanjing University

Biography

I am a fourth year Ph.D. student of School of Artificial Intelligence in Nanjing University and a member of LAMDA Group, which is led by professor Zhi-Hua Zhou.

I received my B.Sc. degree from Department of Mathematics in June 2016 from Nanjing University, and M.Sc. degree from School of Computer Science and Technology in June 2019 from University of Science and Technology of China. In September 2020, I was admitted to study for a Ph.D. degree in Nanjing University.

Research Interests

I am interested in theoretical analysis of evolutionary algorithms under uncertain environments.

Publications

Journal Article

  1. 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]

  2. 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]

  3. 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]

  4. Chao Qian, Chao Bian, Yang Yu, Ke Tang, and Xin Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails.
    Algorithmica, 2021, 83(4): 940-975. [Preprint PDF][PDF]

  5. 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(2): 749-795. [Preprint PDF][PDF]

Conference Paper

  1. 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]

  2. 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)

  3. 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]

  4. 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]

  5. 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)

  6. 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]

  7. 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)

  8. 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)

  9. 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]

  10. 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]

  11. 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]

  12. 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]

Awards & Honors

National Graduate Scholarship, 2018.

Professional Activities

Journal Reviewers

Conference PC Members

Correspondence

Email: bianc@lamda.nju.edu.cn

Office: Room A516, YiFu Building, Xianlin Campus of Nanjing University

Mail Address: National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus Mailbox 603, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
(南京市栖霞区仙林大道163号, 南京大学仙林校区603信箱, 软件新技术国家重点实验室, 210023)