唐珂

教授
智能科学与工程创新中心
E-mail:tangk3@sustc.edu.cn
个人主页


研究方向
计算智能、演化计算、机器学习


教育背景
2003-2007，南洋理工大学，博士
1998-2002，华中科技大学，学士


工作经历
2017-至今南方科技大学计算机科学与工程系教授
2007-2017 中国科学技术大学计算机科学与技术学院副教授、教授


荣誉与奖项
英国皇家学会牛顿高级****
2015年中国电子学会自然科学一等奖（第3完成人）
2012年教育部新世纪优秀人才
2011年教育部自然科学二等奖（第1完成人）


代表文章
1. B. Li, K. Tang, J. Li and X. Yao, “Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators,” IEEE Transactions on Evolutionary Computation, 20(6): 924-938, December 2016.
2. Y. Sun, K. Tang, L. L. Minku, S. Wang and X. Yao, “Online Ensemble Learning of Data Streams with Gradually Evolved Classes,” IEEE Transactions on Knowledge and Data Engineering, 28(6): 1532-1545, June 2016.
3. K. Tang, P. Yang and X. Yao, “Negatively Correlated Search,” IEEE Journal on Selected Areas in Communications, 34(3): 1-9, March 2016.
4. J. Wang, K. Tang, J. A. Lozano and X. Yao, “Estimation of Distribution Algorithm with Stochastic Local Search for Uncertain Capacitated Arc Routing Problems,” IEEE Transactions on Evolutionary Computation, 20(1): 96-109, February 2016.
5. P. Yang, K. Tang, J. A. Lozano and X. Cao, “Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints,” IEEE Transactions on Robotics, 31(5): 1130-1146, October 2015.
6. H. Fu, B. Sendhoff, K. Tang and X. Yao, “Robust Optimization Over Time: Problem Difficulties and Benchmark Problems,” IEEE Transactions on Evolutionary Computation, 19(5): 731-745, October 2015.
7. M. Omidvar, X. Li and K. Tang, “Designing Benchmark Problems for Large-Scale Continuous Optimization,” Information Sciences, 316: 419-436, September 2015.
8. B. Li, J. Li, K. Tang and X. Yao, “Many-Objective Evolutionary Algorithms: A Survey,” ACM Computing Surveys, 48(1), Article 13, 35 pages, September 2015.
9. P. Yang, K. Tang and X. Lu, “Improving Estimation of Distribution Algorithm on Multimodal Problems by Detecting Promising Areas,” IEEE Transactions on Cybernetics, 45(8): 1438-1449, August 2015.
10. L. Wan, K. Tang, M. Li, Y. Zhong and A. K. Qin, “Collaborative Active and Semi-supervised Learning for Hyperspectral Remote Sensing Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2384-2396, May 2015.
11. P. Wang, M. Emmerich, R. Li, K. Tang, T. Baeck and X. Yao, “Convex Hull-Based Multi-objective Genetic Programming for Maximizing Receiver Operating Characteristic Performance,” IEEE Transactions on Evolutionary Computation, 19(2): 188-200, April 2015.
12. X. Yang, K. Tang and X. Yao, “A Learning-to-Rank Approach to Software Defect Prediction,” IEEE Transactions on Reliability, 64(1): 234-246, March 2015.
13. L. Li and K. Tang, “History-Based Topological Speciation for Multimodal Optimization,” IEEE Transactions on Evolutionary Computation, 19(1): 136-150, February 2015.
14. K. Tang, F. Peng, G. Chen and X. Yao, “Population-based Algorithm Portfolios with automated constituent algorithms selection,” Information Sciences, 279: 94-104, September 2014.
15. T. Weise, M. Wan, P. Wang, K. Tang, A. Devert and X. Yao, “Frequency Fitness Assignment,” IEEE Transactions on Evolutionary Computation, 18(2): 226-243, April 2014.
16. M. Lin, K. Tang and X. Yao, “Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification,” IEEE Transactions on Neural Networks and Learning Systems, 24(4): 647-660, April 2013.
17. Z. Yang, X. Li, C. P. Bowers, T. Schnier, K. Tang and X. Yao, “An Efficient Evolutionary Approach to Parameter Identification in a Building Thermal Model,” IEEE Transactions on Systems, Man, and Cybernetics: Part C, 42(6): 957-969, November 2012.
18. X. Lu and K. Tang, “Classification- and Regression-Assisted Differential Evolution for Computationally Expensive Problems,” Journal of Computer Science and Technology, 27(5): 1024-1034, September 2012.
19. T. Chen, K. Tang, G. Chen and X. Yao, “A Large Population Size Can Be Unhelpful in Evolutionary Algorithms,” Theoretical Computer Science, 436: 54-70, June 2012.
20. T. Weise and K. Tang, “Evolving Distributed Algorithms with Genetic Programming,” IEEE Transactions on Evolutionary Computation, 16(2): 242-265, April 2012.
21. A. Devert, T. Weise and K. Tang, “A Study on Scalable Representations for Evolutionary Optimization of Ground Structures,” Evolutionary Computation, 20(3): 453-472, January 2012.
22. Y. Mei, K. Tang and X. Yao, “A Memetic Algorithm for Periodic Capacitated Arc Routing Problem,” IEEE Transactions on Systems, Man, and Cybernetics: Part B, 41(6): 1654-1667, December 2011.
23. Y. Mei, K. Tang and X. Yao, “Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem,” IEEE Transactions on Evolutionary Computation, 15(2): 151-165, April 2011.
24. Z. Wang, K. Tang and X. Yao, “A Memetic Algorithm for Multi-level Redundancy Allocation,” IEEE Transactions on Reliability, 59(4): 754-765, December 2010.
25. F. Peng, K. Tang, G. Chen and X. Yao, “Population-based Algorithm Portfolios for Numerical Optimization,” IEEE Transactions on Evolutionary Computation, 14(5): 782-800, October 2010.
26. Z. Wang, K. Tang and X. Yao, “Multi-objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems,” IEEE Transactions on Reliability, 59(3): 563-575, September 2010.
27. T. Chen, K. Tang, G. Chen and X. Yao, “Analysis of Computational Time of Simple Estimation of Distribution Algorithms,” IEEE Transactions on Evolutionary Computation, 14(1): 1-22, February 2010.
28. K. Tang, Y. Mei and X. Yao, “Memetic Algorithm with Extended Neighborhood Search for Capacitated Arc Routing Problems,” IEEE Transactions on Evolutionary Computation, 13(5): 1151-1166, October 2009.
29. Y. Mei, K. Tang and X. Yao, “A Global Repair Operator for Capacitated Arc Routing Problem,” IEEE Transactions on Systems, Man, and Cybernetics: Part B, 39(3): 723-734, June 2009.
30. G. Pugalenthi, K. Tang, P. N. Suganthan and S. Chakrabarti, “Identification of Structurally Conserved Residues of Proteins in Absence of Structural Homologs Using Neural Network Ensemble,” Bioinformatics, 25(2): 204-210, January 2009.
31. Z. Yang, K. Tang and X. Yao, “Large Scale Evolutionary Optimization Using Cooperative Coevolution,” Information Sciences, 178(15): 2985-2999, August 2008. (According to ESI, it has been selected as the Highly Cited Papers in Computer Science for the past 11 years.)
32. E. K. Tang, P. N. Suganthan and X. Yao, “Gene Selection Algorithms for Microarray Data Based on Least Squares Support Vector Machine,” BMC-Bioinformatics, 7:95, 27 February 2006.
33. E. K. Tang, P. N. Suganthan, X. Yao, “An Analysis of Diversity Measures,” Machine Learning, 65: 247-271, October 2006.
34. E. K. Tang, P. N. Suganthan and X. Yao and A. K. Qin, “Linear Dimensionality Reduction Using Relevance Weighted LDA,” Pattern Recognition, 38(4): 485-493, April 2005.
35. C. Qian, J.-C. Shi, Y. Yu, K. Tang and Z.-H. Zhou, “Parallel Pareto Optimization for Subset Selection,” in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, July 9-15, 2016, pp.1939-1945.
36. L. Zhang, K. Tang and X. Yao, “Increasingly Cautious Optimism for Practical PAC-MDP Exploration,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, July 25-31, 2015, pp. 4033-4040.
37. J. Zhong, K. Tang and Z.-H. Zhou, “Active Learning from Crowds with Unsure Option,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, July 25-31, 2015, pp. 1061-1067.
38. T. Chen, Q. Guo, K. Tang, O. Temam, Z. Xu, Z.-H. Zhou, and Y. Chen, “ArchRanker: A ranking approach to design space exploration,” in Proceedings of the 41st International Symposium on Computer Architecture (ISCA’14), Minneapolis, MN, 2014, pp.85-96.
39. R. Wang, W. Dong, Y. Wang, K. Tang and X. Yao, “Pipe Break Prediction: A Data Mining Method,” in Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE’13), Brisbane, Australia, April 8-11, 2013, pp. 1208-1218.
40. K. Tang, R. Wang and T. Chen, “Towards Maximizing The Area Under The ROC Curve For Multi-class Classification Problems,” in Proceedings of The 25th AAAI Conference on Artificial Intelligence (AAAI 2011), San Francisco, USA, 7-11 August 2011, pp. 483-488.