1. 简单百科
  2. 唐珂

唐珂

唐珂，男，1981年7月出生，博士，教授、自然计算与应用实验室常务副主任。南方科技大学计算机科学与工程系教授。主要研究领域为人工智能的共性原理、算法（如演化计算、强化学习、机器学习等），以及人工智能与设计、金融、物流等领域的交叉研究。现兼任广东省类脑智能计算重点实验室副主任、深圳斯发基斯可信自主系统研究院副院长。

人物经历

2018 – 至今：南方科技大学，计算机科学与工程系，教授

2011 – 2018：中国科学技术大学，计算机科学与技术学院，教授

2007 – 2011：中国科学技术大学，计算机科学与技术学院，副教授

2003 – 2007：南洋理工大学，博士

1998 – 2002：华中科技大学，学士

获得荣誉

教育部特聘教授

国家万人计划青年拔尖人才

Outstanding Early Career Award, IEEE Computational Intelligence Society

英国皇家学会艾萨克·牛顿高级学者 Newton Advanced Fellowship, Royal Society (UK)

教育部新世纪优秀人才

教育部自然科学一等奖（第4完成人）

教育部自然科学二等奖（第 1 完成人）

中国电子学会自然科学一等奖（第 3 完成人）

2022年11月21日，美国电子电气工程师学会公布了新一届Fellow名单，唐珂在列。

社会任职

IEEE Transactions on Evolutionary Computation 副编

Swarm and Evolutionary Computation (Elsevier) 副编

十余次担任 IEEE-CEC、SEAL、IDEAL 等国际会议程序/技术委员会主席

中国演化计算与学习研讨会（ECOLE）创始执委

IEEE大规模全局优化工作组主席（IEEE CIS，进化计算技术委员会）、《信息科学杂志》（Elsevier）特刊客座编辑、项目委员会成员。

社会活动

2007年8月19日至22日在香港特别行政区参加第六届机器学习和控制论国际会议（ICMLC2007）。9月6-7日在爱尔兰都柏林参加IEEE SMC UK\u0026RI第六届控制论系统会议。

2008年3月26日至28日在意大利那不勒斯参加第一届欧洲连续参数优化仿生算法研讨会（EvoNUM）。6月18日至20日在波兰弗罗茨瓦夫参加第二十一届应用智能系统工业、工程及其他应用国际会议（IEA-AIE2008）。7月12-15日在昆明市参加第7届机器学习与控制论国际会议（ICMLC2008）。8月18-22日在北爱尔兰参加进化计算研讨会暨暑期学校（WSSEC2008）。9月9日至10日在英国伦敦参加第七届IEEE控制论智能系统国际会议。9月24-26日在西班牙布尔戈斯参加第三届混合人工智能系统国际研讨会（HAIS2008）。

主要成就

研究领域

My work is, in general, about Fundamental research on computational approaches for Learning and Optimization, two most important problems in Artificial Intelligence. I'm also frequently attracted by other relevant domains, such as Smart Logistics, Structural and Multi-Disciplinary Optimization and Computational Finance, for which applied reseArch Linux is required to produce application-oriented learning and optimization techniques.

Most of my research could also be viewed as arising from Evolutionary Computation, which is essentially a distributed heuristic search framework widely applicable for modelling, learning and optimization problems, especially for hard problems where limited prior knowledge is available.

Selected topics for Fundamental research are listed below.

1. Scalable Evolutionary Search

Research in this direction aims at systematically boosting the capacity of Evolutionary Computation on problems with huge search space, which has been believed as a major challenge for most EAs. Approaches for this purpose include:

• Co-evolutionary Search: Introducing the divide-and-conquer idea to guide EAs adaptively search different regions of the search space

• Parallel Algorithm Portfolios: Leveraging on high 表演 computing to enhance both the extreme performance and reliability of EAs, without suffering the wall-clock runtime but only computational resources.

• Surrogate-assisted Search: Exploiting data generated during the search course to alleviate the cost of evaluating a future solution.

2. Reinforcement and Evolutionary Learning

Reinforcement Learning is a learning problem that lies exactly in the "backyard" of EAs, because the objective function of most RL tasks so far rely on a noisy and non-differentiable simulator. Thus it’d be quite interesting to see whether EC could offer a promising alternative approach for RL.

3. Learning and Optimization with Uncertainty

Uncertainty is ubiquitous in real-world learning and 最优化 tasks. It could be due to the dynamically changing physical world, the noise caused by imprecise measurements, or even the unpredictable nature of human behaviors. We are specifically interested in new learning/optimization methods that could handle various forms of uncertainty. This has led to exploration on the following topics:

• Incremental learning with concept drift

• Evolutionary computation for Dynamic optimization

• Learning from crowds (Crowdsourcing Learning)

论文著作

Corresponding Author

Z. Liu, B. Wang and K. Tang*, “Handling Constrained Multi-Objective Optimization Problems via Bidirectional Coevolution,” IEEE Transactions on Cybernetics, in press (DOI: 10.1109/TCYB.2021.3056176).

Y. Lei and K. Tang*, “Learning Rates for Stochastic Gradient Descent with Nonconvex Objectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, in press (DOI: 10.1109/TP急性心肌梗死2021.3068154).

C. Bian, C. Qian, Y. Yu and K. Tang, “On the Robustness of Median Sampling in Noisy Evolutionary Optimization,” SCIENCE CHINA Information Sciences, accepted on October 16, 2020.

P. Yang, Q. Yang, K. Tang* and X. Yao, “Parallel Exploration via Negatively Correlated Search,” Frontiers of Computer Science, in press (DOI: 10.1007/s11704-020-0431-0), 2020.

C. Hou, H. Zhang, S. He and K. Tang*, “GloDyNE: Global Topology Preserving Dynamic Network Embedding,” IEEE Transactions on Knowledge and Data Engineering, in press (DOI: 10.1109/TKDE.2020.3046511).

K. Tang*, S. Liu, P. Yang and X. Yao, “Few-shots Parallel Algorithm Portfolio Construction via Co-evolution,” IEEE Transactions on Evolutionary Computation, in press (DOI: 10.1109/TEVC.2021.3059661).

W. Hong, P. Yang and K. Tang*, “Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses,” International Journal of 自动化技术 and Computing, in press (DOI: 10.1007/s11633-020-1253-0).

T. Sun, K. Tang* and D. Li, “Gradient Descent Learning with Floats,” IEEE Transactions on Cybernetics, in press (DOI: 10.1109/TCYB.2020.2997399).

S. Liu, K. Tang* and X. Yao, “Generative Adversarial Construction of Parallel Portfolios,” IEEE Transactions on Cybernetics, in press (DOI: 10.1109/TCYB.2020.2984546).

W. Hong, C. Qian and K. Tang, “Efficient Minimum Cost Seed Selection with Theoretical Guarantees for Competitive Influence Maximization,” IEEE Transactions on Cybernetics, in press (DOI: 10.1109/TCYB.2020.2966593).

C. Qian, C. Bian, Y. Yu, K. Tang* and Xin Yao, “Analysis of Noisy Evolutionary Optimization When Sampling Fails,” Algorithmica, in press (DOI: 10.1007/s00453-019-00666-6).

Y. Lei, T. Hu and K. Tang, “Generalization 表演 of Multi-pass Stochastic Gradient Descent with Convex Loss Functions,” Journal of Machine Learning Research, 22: 1-41, January 2021.

C. Bian, C. Qian, K. Tang and Y. Yu, “Running 时间 Analysis of the (1+1)-EA for Robust Linear Optimization,” Theoretical Computer Science, 843: 57-72, December 2020.

C. Hou, S. He and K. Tang*, “RoSANE: Robust and Scalable Attributed Network Embedding for Sparse Networks,” Neurocomputing, 409: 231-243, October 2020.

Y. Lei, T. Hu, G. Li and K. Tang, “Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions,” IEEE Transactions on Neural Networks and Learning Systems, 39(10): 4394-4400, October 2020.

F. Wang. Y. Li, A. Zhou and K. Tang, “An Estimation of Distribution Algorithm for Mixed-variable Newsvendor Problems,” IEEE Transactions on Evolutionary Computation, 24(3): 479-493, June 2020.

W. Du, W. Ying, P. Yang, X. Cao, G. Yan, K. Tang and D. Wu, “Network-Based Heterogeneous Particle Swarm Optimization and Its Application in UAV Communication Coverage,” IEEE Transactions on Emerging Topics in Computational Intelligence, 4(3): 312-323, June 2020.

D. Jiao, P. Yang, L. Fu, L. Ke and K. Tang, “Optimal 能量-Delay Scheduling for Energy-Harvesting WSNs with Interference Channel via Negatively Correlated Search,” IEEE Internet of Things Journal, 7(3): 1690-1703, March 2020.

D. Wu, N. Jiang, W. Du, K. Tang and X. Cao, “Particle Swarm Optimization with Moving Particles on Scale-free Networks,” IEEE Transactions on Network Science and Engineering, 7(1): 497-506, March 2020.

C. Qian, Y. Yu, K. Tang, X. Yao and Z.-H. Zhou, “Maximizing Submodular or Monotone Approximately Submodular 函数s by Multi-objective Evolutionary Algorithms,” Artificial Intelligence, 275: 279-294, October 2019.

W. Hong, K. Tang*, A. Zhou, H. Ishibuchi and X. Yao, “A Scalable Indicator-Based Evolutionary Algorithm for Large-Scale Multi-Objective Optimization,” IEEE Transactions on Evolutionary Computation, 23(3): 525-537, June 2019.

X. Lu, T. Sun, and K. Tang, “Evolutionary Optimization with Hierarchical Surrogates,” Swarm and Evolutionary Computation, vol. 47, pp. 21-32, June 2019.

X. Ma, X. Li, Q. Zhang, K. Tang, Z. Liang, W. Xie and Z. Zhu, “A Survey on Cooperative Coevolutionary Algorithms,” IEEE Transactions on Evolutionary Computation, 23(3): 421-441, June 2019.

Z.-Z. Liu, Y. Wang, S. Yang and K. Tang, “An Adaptive Framework to Tune The Coordinate Systems in Nature-inspired Optimization Algorithms,” IEEE Transactions on Cybernetics, 49(4): 1403-1416, April 2019.

X. Liang, A. K. Qin, K. Tang* and K. C. Tan, “QoS-Aware Web Service Selection with Internal Complementarity,” IEEE Transactions on Services Computing, 12(2): 276-289, March 2019.

C. Qian, C. Bian, W. Jiang and K. Tang*, “Running 时间 Analysis of the (1+1)-EA for OneMax and LeadingOnes Under Bit-Wise Noise,” Algorithmica, 81(2): 749-795, February 2019.

W. Du, M. Zhang, W. Ying, M. Perc, K. Tang, X. Cao and D. Wu, “The networked evolutionary algorithm: A network science perspective,” Applied Mathematics and Computation, 338: 33-43, December 2018.

Y. Sun, K. Tang*, Z. Zhu and X. Yao, “Concept Drift Adaptation by Exploiting Historical Knowledge,” IEEE Transactions on Neural Networks and Learning Systems, 29(10): 4822-4832, October 2018.

C. Qian, J. Shi, K. Tang and Z.-H. Zhou, “Constrained Monotone k-Submodular 函数 Maximization Using Multiobjective Evolutionary Algorithms with Theoretical Guarantee,” IEEE Transactions on Evolutionary Computation, 22(4): 595-608, August 2018.

J. Zhang, A. Zhou, K. Tang and G. Zhang, “Preselection via classification: A case study on evolutionary multiobjective optimization,” Information Sciences, 465: 388-403, July 2018.

C. Qian, Y. Yu, K. Tang, Y. Jin, X. Yao and Z.-H. Zhou, “On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments,” Evolutionary Computation, 26(2): 237-267, June 2018.

X. Lu, S. Menzel, K. Tang* and X. Yao, “Cooperative Co-evolution based 设计 Optimisation: A Concurrent Engineering Perspective,” IEEE Transactions on Evolutionary Computation, 22(2): 173-188, April 2018.

P. Yang, K. Tang* and X. Yao, “Turning High-dimensional Optimization into Computationally Expensive Optimization,” IEEE Transactions on Evolutionary Computation, 22(1): 143-156, February 2018.

J. Zhong, P. Yang, K. Tang*, “A Quality-Sensitive Method for Learning from Crowds,” IEEE Transactions on Knowledge and Data Engineering, 29(12): 2643-2654, December 2017.

K. Cai, J. Zhang, M. Xiao, K. Tang and W. Du, “Simultaneous Optimization of Airspace Congestion and Flight Delay in Air Traffic Network Flow Management,” IEEE Transactions on Intelligent Transportation Systems, 18(11): 3072-3082, November 2017.

K. Tang, J. Wang X. Li and X. Yao, “A Scalable Approach to Capacitated Arc Routing Problems Based on Hierarchical Decomposition,” IEEE Transactions on Cybernetics, 47(11): 3928-3940, November 2017.

Y. Zhang, Y. Mei, K. Tang and K. Jiang, “Memetic algorithm with route decomposing for periodic capacitated arc routing problem,” Applied Soft Computing, 52: 1130-1142, March 2017.

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.

S. He, G. Jia, Z. Zhu, Q. Huang, K. Tang, J. Liu, M. Musolesi, J. K. Heath and X. Yao, “Cooperative Co-Evolutionary Module Identification with Application to Cancer Disease Module Discovery,” IEEE Transactions on Evolutionary Computation, 20(6): 874-891, December 2016.

T. Weise, Y. Wu, R. Chiong, K. Tang and J. Lässig, “Global versus local search: the impact of population sizes on evolutionary algorithm 表演,” Journal of Global Optimization, 66(3): 511-534, November 2016.

Z. Yang, B. Sendhoff, K. Tang and X. Yao, “Target shape 设计 optimization by evolving B-splines with cooperative coevolution,” Applied Soft Computing, 48: 672-682, November 2016.

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.

K. Tang, P. Yang and X. Yao, “Negatively Correlated Search,” IEEE Journal on Selected Areas in Communications, 34(3): 1-9, March 2016.

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.

W. Hong and K. Tang*, “Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance,” Memetic Computing, 8(1): 35-44, February 2016.

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.

H. Fu, B. Sendhoff, K. Tang and X. Yao, “Robust Optimization Over 时间: Problem Difficulties and Benchmark Problems,” IEEE Transactions on Evolutionary Computation, 19(5): 731-745, October 2015.

M. Omidvar, X. Li and K. Tang, “Designing Benchmark Problems for Large-Scale Continuous Optimization,” Information Sciences, 316: 419-436, September 2015.

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.

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.

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.

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 表演,” IEEE Transactions on Evolutionary Computation, 19(2): 188-200, April 2015.

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.

L. Li and K. Tang*, “History-Based Topological Speciation for Multimodal Optimization,” IEEE Transactions on Evolutionary Computation, 19(1): 136-150, February 2015.

X. Lu, K. Tang*, B. Sendhoff and X. Yao, “A New Self-adaptation Scheme for Differential Evolution,” Neurocomputing, 146: 2-16, December 2014.

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.

T. Weise, R. Chiong, J. Lassig, K. Tang, S. Tsutsui, W. Chen, Z. Michalewicz and X. Yao, “Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem,” IEEE Computational Intelligence Magazine, 9(3): 40-52, August 2014. (This paper was highlighted by the IEEE Computational Intelligence Magazine as “Publication Spotlight” in its August 2014 issue, Page 12.)

T. Weise, M. Wan, P. Wang, K. Tang, A. Devert and X. Yao, “频率 Fitness Assignment,” IEEE Transactions on Evolutionary Computation, 18(2): 226-243, April 2014.

X. Lu, K. Tang, B. Sendhoff and X. Yao, “A Review of Concurrent Optimization Methods,” International Journal of Bio-inspired Computation, 6(1): 22-31, March 2014.

P. Wang, K. Tang*, T. Weise, E. P. K. Tsang and X. Yao, “Multiobjective Genetic Programming for Maximizing ROC 表演,” Neurocomputing, 125: 102-118, February 2014.

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.

Y. Jin, K. Tang*, X. Yu, B. Sendhoff and X. Yao, “A framework for finding robust optimal solutions over time,” Memetic Computing, 5(1): 3-18, March 2013.

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.

K. Cai, J. Zhang, C. Zhou, X. Cao and K. Tang, “Using computational intelligence for large scale air route networks 设计,” Applied Soft Computing, 12(9): 2790-2800, September 2012.

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.

T. Weise, R. Chiong and K. Tang, “Evolutionary Optimization: Pitfalls and Booby Traps,” Journal of Computer Science and Technology, 27(5): 907-936, September 2012.

R. Wang and K. Tang*, “Feature Selection for MAUC Oriented Classification Systems,” Neurocomputing, 89: 39-54, July 2012.

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.

T. Weise and K. Tang, “Evolving Distributed Algorithms with Genetic Programming,” IEEE Transactions on Evolutionary Computation, 16(2): 242-265, April 2012.

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.

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.

Z. Yang, K. Tang* and X. Yao, “Scalability of Generalized Adaptive Differential Evolution for Large-Scale Continuous Optimization,” Soft Computing, 15(11): 2141-2155, November 2011.

X. Yu, K. Tang* and X. Yao, “Immigrant Schemes for Evolutionary Algorithms in Dynamic Environments: Adapting the Replacement Rate,” Science in China Series F: Information Sciences, 54(7): 1352-1364, July 2011.

D. Liu, K. Tang, Z. Yang and D. Liu, “A Fiber Bragg Grating Sensor Network Using an Improved Differential Evolution Algorithm,” IEEE Photonics Technology Letters, 23(19): 1385-1387, June 2011.

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.

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.

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.

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.

T. Chen, K. Tang*, G. Chen and X. Yao, “Analysis of Computational 时间 of Simple Estimation of Distribution Algorithms,” IEEE Transactions on Evolutionary Computation, 14(1): 1-22, February 2010.

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.

K. Tang, M. Lin, F. L. Minku and X. Yao, “Selective Negative Correlation Learning Approach to Incremental Learning,” Neurocomputing, 72(13-15): 2796-2805, August 2009.

K. Tang, G. Pugalenthi, P. N. Suganthan, C. J. Lanczycki and S. Chakrabarti, “Prediction of Functionally Important Sites from Protein Sequences Using Sparse Kernel Least Squares Classifiers,” Biochemical and Biophysical Research Communications, 384(2): 155-159, June 2009.

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.

X. Yu, K. Tang*, T. Chen and X. Yao, “Empirical Analysis of Evolutionary Algorithms with Immigrants Schemes for Dynamic Optimization,” Memetic Computing, 1(1): 3-24, March 2009.

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,” 生物信息学, 25(2): 204-210, January 2009.

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.)

G. Pugalenthi, K. Tang, P. N. Suganthan, G. Archunan and R. Sowdhamini, “A Machine Learning Approach for The Identification of Odorant Binding Proteins from Sequence-derived Properties,” BMC生物信息学, 8:351, September 2007. (This paper was highlighted by ScienceWatch.com as a “New Hot Paper”. The report of an online interview of the authors is available at: http://archive.sciencewatch.com/dr/nhp/2009/09marnhp/09marnhpRamET/)

E. K. Tang, P. N. Suganthan and X. Yao, “Gene Selection Algorithms for Microarray Data Based on Least Squares Support Vector Machine,” BMC-生物信息学, 7:95, 27 February 2006.

E. K. Tang, P. N. Suganthan, X. Yao, “An Analysis of Diversity Measures,” Machine Learning, 65: 247-271, October 2006.

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.

Refereed Papers in Conference Proceedings

W. Hong, P. Yang, Y. Wang and K. Tang, “Multi-objective Magnitude-Based Pruning for Latency-Aware Deep Neural Network Compression,” in Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN'20), Leiden, Netherlands, September 5-9, 2020, pp.470-483.

S. Liu, K. Tang, Y. Lei and X. Yao, “On 表演 Estimation in Automatic Algorithm Configuration,” in Proceedings of The 34th AAAI Conference on Artificial Intelligence (AAAI-2020), New York, USA, February 7-12, 2020.

L. Zhang, K. Tang and X. Yao, “Explicit Planning for Efficient Exploration in Reinforcement Learning,” In: Advances in Neural Information Processing Systems (NIPS'19),温哥华, Canada, December 08-14, 2019, pp. 7488-7497.

Y. Lei, P. Yang, K. Tang and D. X. Zhou, “Optimal Stochastic and Online Learning with Individual Iterates,” In: Advances in Neural Information Processing Systems (NIPS'19),温哥华, Canada, December 08-14, 2019, pp. 5416-5426. (Spotlight, 164 out of 6743 submissions)

D. Jiao, P. Yang, L. Fu, L. Ke and K. Tang, “Optimal 能量Delay Scheduling for Energy Harvesting WSNs via Negatively Correlated Search,” in Proceedings of ICC 2019-2019 IEEE International Conference on Communications (ICC), Shanghai, China, May 20-24, 2019.

S. Liu, K. Tang and X. Yao, “Automatic Construction of Parallel Portfolios via Explicit Instance Grouping,” in Proceedings of The 33th AAAI Conference on Artificial Intelligence (AAAI-2019), Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp. 1560-1567.

C. Feng, C. Qian and K. Tang, “Unsupervised Feature Selection by Pareto Optimization,” in Proceedings of The 33th AAAI Conference on Artificial Intelligence (AAAI-2019), Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp. 3534-3541.

Y. Lei and K. Tang, “Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities,” In: Advances in Neural Information Processing Systems 30 (NIPS'18), Montréal, Canada, December 2-8, 2018, pp. 1526-1536.

C. Bian, C. Qian and K. Tang, “A General Approach to Running 时间 Analysis of Multi-objective Evolutionary Algorithms,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, July 13-19, 2018, pp. 1405-1411.

G.-Y. Li, C. Qian, C.-H. Jiang, X. 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, July 13-19, 2018, pp. 2383-2389.

C. Qian, Y. Yu and K. 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, July 13-19, 2018, pp 1478-1484.

Y.-W. Lei, S.-B. Lin and K. Tang, “Generalization Bounds for Regularized Pairwise Learning,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, July 13-19, 2018, pp 2376-2382.

C.-H. Jiang, G.-Y. Li, C. Qian and K. 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, July 13-19, 2018, pp 2298-2304.

C. Qian, C. Feng and K. Tang, “Sequence Selection by Pareto Optimization,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, July 13-19, 2018, pp 1485-1491.

C. Qian, G.-Y. Li, C. Feng and K. Tang, “Distributed Pareto Optimization for Subset Selection,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, July 13-19, 2018, pp. 1492-1498.

L. Zhang, K. Tang and X. Yao, “Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning,” In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, December 4-9, 2017, pp1802--1812.

C. Qian, J. Shi, Y. Yu, K. Tang and Z.-H. Zhou, “Subset Selection under Noise,” In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, December 4-9, 2017, pp.3563-3573.

C. Qian, J. Shi, Y. Yu and K. Tang, “On Subset Selection with General Cost Constraints,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 墨尔本, Australia, 2017, pp2613-2619.

C. Qian, J. Shi, Y. Yu, K. Tang and Z.-H. Zhou, “Optimizing Ratio of Monotone Set Functions,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 墨尔本, Australia, 2017, pp.2606-2612.

C. Qian, C. Bian, W. Jiang and K. 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, pp1399-1406.

C. Qian, K. Tang and Z.-H. 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), 爱丁堡, Scotland, September 17-21, 2016, pp835-846.

J. Fu, J. Zhong, Y. Liu, Z. Wang and K. Tang, “A Non-parametric Approach for Learning from Crowds,” in Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN’16), 温哥华, Canada, July 24-29, 2016, pp 2228-2235.

C. Jiang, G. Li, J. Liu, Y. Liu and K. Tang, “A Trajectory-based Approach for Object Detection from Video,” in Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN’16), 温哥华, Canada, July 24-29, 2016, pp. 2887-2893.

P. Yang, G. Lu, K. Tang and X. Yao, “A Multi-Modal Optimization Approach to Single Path Planning for Unmanned Aerial Vehicle,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC’16), 温哥华, Canada, July 24-29, 2016, pp. 1735-1742.

B. Li, C. Qian, J. Li, K. Tang and X. Yao, “Search Based Recommender System Using Many-Objective Evolutionary Algorithm,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC’16), 温哥华, Canada, July 24-29, 2016, pp. 120-126.

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.

L. Zhang, K. Tang and X. Yao, “Increasingly Cautious Optimism for Practical PACMDP Exploration,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, July 25-31, 2015, pp. 4033-4040.

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.

Y. Wu, Y. Sun, X. Liang, K. Tang and Z. Cai, “Evolutionary Semi-Supervised Ordinal Regression Using Weighted Kernel Fisher Discriminant Analysis,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, Japan, May 25-28, 2015, pp 3279-3286.

S. Liu, Y. Wei, K. Tang, A. K. Qin and X. Yao, “QoS-aware Long-term Based Service Composition in Cloud Computing,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, Japan, May 25-28, 2015, pp. 3362-3369.

W. Hong, G. Lu, P. Yang, Y. Wang and K. Tang, “A New Evolutionary Multi-objective Algorithm for Convex Hull Maximization,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, Japan, May 25-28, 2015, pp 931-938.

X. Lu, S. Menzel, K. Tang and X. Yao, “The 表演 Effects of Interaction 频率 in Parallel Cooperative Coevolution,” in Proceedings of the 10th International Conference on Simulated Evolution And Learning (SEAL 2014), December 15-18, 2014, Lecture Notes in Computer Science Volume 8886, 2014, pp.82-93, Springer-Verlag, Berlin.

Z. Miao, J. Wang, A. Zhou and K. Tang, “Regularized Boost for Semi-supervised Ranking,” in Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES2014), November 10-12, 2014, Singapore, Proceedings in Adaptation, Learning and Optimization Volume 1, 2015, pp. 643-651.

P. Yang, K. Tang, L. Li and A. K. Qin, “Evolutionary Robust Optimization with Multiple Solutions,” in Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES2014), November 10-12, 2014, Singapore, Proceedings in Adaptation, Learning and Optimization Volume 1, 2015, pp. 611-625.

T. Chen, Q. Guo, K. Tang, O. Temam, Z. Xu, Z.-H. Zhou, and Y. Chen, “ArchRanker: A ranking approach to 设计 space exploration,” in Proceedings of the 41st International Symposium on Computer 建筑 (ISCA’14), Minneapolis, MN, 2014, pp.85-96.

H. Fu, P. R. Lewis, B. Sendhoff, K. Tang, and X. Yao, “What Are Dynamic Optimization Problems?” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC2014), Beijing, China, July 6-11, 2014, pp. 1550-1557.

J. Zhong, K. Tang and A. K. Qin, “Finding Convex Hull Vertices in Metric Space,” in Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN2014), Beijing, China, July 6-11, 2014, pp. 1587-1592.

P. Yang, K. Tang and J. A. Lozano, “Estimation of Distribution Algorithms based Unmanned Aerial Vehicle Path Planner Using a New Coordinate System,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC2014), Beijing, China, July 6-11, 2014, pp. 1469-1476.

T. Weise, M. Wan, K. Tang and X. Yao, “Evolving exact integer algorithms with Genetic Programming,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC2014), Beijing, China, July 6-11, 2014, pp1816-1823.

B. Li, J. Li, K. Tang and X. Yao, “An Improved Two Archive Algorithm for Many-Objective Optimization,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC2014), Beijing, China, July 6-11, 2014, pp. 2869-2876.

Z. Miao and K. Tang, “Semi-supervised Ranking via List-wise Approach,” in Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’13), Hefei, China, October 20-23, 2013, pp. 376-383, Lecture Notes in Computer Science, Volume 8206, Springer-Verlag Berlin Heidelberg, Germany.

L. 壮族, K. Tang and Y. Jin, “Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient,” in Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’13), Hefei, China, October 20-23, 2013, pp. 366-375, Lecture Notes in Computer Science, Volume 8206, Springer-Verlag Berlin Heidelberg, Germany.

L. Wan, K. Tang and R. Wang, “Gradient Boosting-based Negative Correlation Learning,” in Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’13), Hefei, China, October 20-23, 2013, pp. 358-365, Lecture Notes in Computer Science, Volume 8206, Springer-Verlag Berlin Heidelberg, Germany.

J. Liu and K. Tang, “Scaling Up Covariance Matrix Adaptation Evolution Strategy using Cooperative Coevolution,” in Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’13), Hefei, China, October 20-23, 2013, pp. 350-357, Lecture Notes in Computer Science, Volume 8206, Springer-Verlag Berlin Heidelberg, Germany.

W. Chen and K. Tang, “Impact of problem decomposition on Cooperative Coevolution,” in Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC’13), Cancun, Mexico, June 20-23, 2013, pp 733-740.

M. Li, R. Wang and K. Tang, “Combining Semi-Supervised and Active Learning for Hyperspectral Image Classification,” in Proceedings of 2013 IEEE Symposium Series on Computational Intelligence (SSCI’13), Singapore, April 16-19, 2013, pp 89-94.

J. Wang, K. Tang and X. Yao, “A Memetic Algorithm for Uncertain Capacitated Arc Routing Problems,” in Proceedings of 2013 IEEE Symposium Series on Computational Intelligence (SSCI’13), Singapore, April 16-19, 2013, pp. 80-87.

R. Wang, W. Dong, Y. Wang, K. Tang and X. Yao, “Pipe Failure Prediction: A Data Mining Method,” in Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE’13), 布里斯班, Australia, April 8-11, 2013, pp. 1208-1218.

Q. Huang, G. Jia, T. White, M. Musolesi, N. Turan, K. Tang, S. He, J. K. Heath and X. Yao, “Community Detection Using Cooperative Co-evolutionary Differential Evolution,” in Proceedings of the 12th International Conference on Parallel Problem Solving From Nature. Taormina, Italy, September 1-5, 2012.

T. Weise, A. Devert and K. Tang, “A Developmental Solution to (Dynamic) Capacitated Arc Routing Problems using Genetic Programming,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'12), 费城, PA, USA, July 7–11, 2012, pp 831-838.

H. Fu, B. Sendhoff, K. Tang, and Xin Yao, “Characterizing environmental changes in Robust Optimization Over 时间,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC2012), Brisbane, Queensland, Australia, 10-15 June 2012, pp. 1-8.

L. Chen, H. Chen and K. Tang, “Semi-supervised Learning with Extremely Sparse Labeled Data on Multiple Semi-supervised Assumptions,” in Proceedings of The 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), 大连市, China, 14-16 October 2011, pp 242-247.

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.

X. Lu, K. Tang, and X. Yao, “Classification-Assisted Differential Evolution for Computationally Expensive Problems,” in Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC2011), New Orleans, USA, 5-8 June 2011, pp. 1986-1993.

P. Wang, K. Tang, E.P.K. Tsang and X. Yao, “A Memetic Genetic Programming with Decision Tree-based Local Search for Classification Problems,” in Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC2011), New Orleans, USA, 5-8 June 2011, pp. 916-923.

X. Fan, K. Tang and T. Weise, “Margin-Based Over-Sampling Method for Learning From Imbalanced Datasets,” in Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011), Shenzhen, China, 24-27 May 2011, pp. 309-320.

M. Wan, T. Weise and K. Tang, “Novel Loop Structures and the Evolution of Mathematical Algorithms,” in Proceedings of the 14th European Conference on Genetic Programming (EuroGP'11), Torino, Italy, 27-29 April 2011, pp 300-309, Lecture Notes in Computer Science, Volume 6621, Springer-Verlag, Berlin, Germany.

W. Chen, T. Weise, Z. Yang and K. Tang, “Large-Scale Global Optimization using Cooperative Coevolution with Variable Interaction Learning,” in Proceedings of the 11th International Conference on Parallel Problem Solving From Nature (PPSN), Kraków, Poland, September 11–15, 2010, pp. 300–309, Lecture Notes in Computer Science, Volume 6239, Part II, Springer-Verlag, Berlin, Germany.

X. Fan and K. Tang, “Enhanced Maximum AUC Linear Classifier,” in Proceedings of The 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2010), Yantai, China, 10-12 August 2010, vol. 4, pp. 1540-1544.

P. Wang, E. P. K. Tsang, T. Weise, K. Tang and X. Yao, “Using GP to Evolve Decision Rules for Classification in Financial Data Sets,” in Proceedings of the 9th IEEE International Conference on Cognitive Informatics (ICCI 2010), Beijing, China, 7-9 July 2010, pp. 722-727.

T. Weise, L. Niu and K. Tang, “AOAB – Automated Optimization Algorithm Benchmarking,” in Proceedings of the 2010 Genetic and Evolutionary Computation Conference (GECCO-2010), Portland, USA, 7-11 July 2010, pp. 1479-1486.

X. Lu, K. Tang and X. Yao, “Evolving Neural Networks with Maximum AUC for Imbalanced Data Classification,” in Proceedings of the 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS2010), San Sebastián, Spain, 23-25 June 2010, Lecture Notes in Computer Science, Volume 6076, Springer-Verlag, Berlin pp. 335-342.

X. Yu, Y. Jin, K. Tang and X. Yao, “Robust Optimization over 时间 - A New Perspective on Dynamic Optimization Problems,” in Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC2010), Barcelona, Spain, 18-23 July 2010, pp 3998-4003.

Y. Mei, K. Tang and X. Yao, “Capacitated Arc Routing Problem in Uncertain Environments,” in Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC2010), Barcelona, Spain, 18-23 July 2010, pp. 1400-1407.

H. Fu, Y. Mei, K. Tang and Y. Zhu, “Memetic Algorithm with Heuristic Candidate List Strategy for Capacitated Arc Routing Problem,” in Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC2010), Barcelona, Spain, 18-23 July 2010, pp. 3229-3236.

R. Wang and K. Tang, “Feature Selection for Maximizing the Area Under the ROC Curve,” in Proceedings of the 2009 International Conference on Data Mining - Workshops, Miami, USA, 6-9 December 2009, pp. 400-405.

X. Yang, K. Tang and X. Yao, “The Minimum Redundancy - Maximum Relevance Approach to Building Sparse Support Vector Machines,” in Proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2009), Lecture Notes in Computer Science, Volume 5788, Springer-Verlag, Berlin, September 2009, pp. 184-190.

S. Wang, K. Tang and X. Yao, “Diversity Exploration and Negative Correlation Learning on Imbalanced Data Sets,” in Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN2009), Atlanta, USA, 14-19 June 2009, pp. 3259-3266.

T. Chen, K. Tang, G. Chen and X. Yao, “Rigorous Time Complexity Analysis of Univariate Marginal Distribution Algorithm with Margins,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 2157-2164.

T. Chen, P. K. Lehre, K. Tang and X. Yao, “When Is an Estimation of Distribution Algorithm Better than an Evolutionary Algorithm?,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 1470-1477.

Y. Chen, K. Tang and T. Chen, “A Stochastic Method for Controlling the Scaling Parameters of Cauchy Mutation in Fast Evolutionary Programming,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 1101-1107.

Y. Mei, K. Tang and X. Yao, “Improved Memetic Algorithm for Capacitated Arc Routing Problem,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 1699-1706.

F. Peng, K. Tang, G. Chen and X. Yao, “Multi-start JADE with knowledge transfer for numerical optimization,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 1889-1895.

Z. Wang, T. Chen, K. Tang and X. Yao, “A Multi-objective Approach to Redundancy Allocation Problem in Parallel-series Systems,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 582-589.

Z. Yang, J. Zhang, K. Tang, X. Yao and A. Sanderson, “An Adaptive Coevolutionary Differential Evolution Algorithm for Large-scale Optimization,” in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, 18-21 May 2009, pp. 102-109.

Z. Wang, Z. Yang, K. Tang, and X. Yao, “Adaptive Differential Evolution for Multi-objective Optimization,” in Proceedings of the 20th International Conference on Multiple Criteria Decision Making (MCDM'09), Chengdu, China, 2009, pp. 9-16.

M. Lin, K. Tang and X. Yao, “Selective Negative Correlation Learning Algorithm for Incremental Learning,” in Proceedings of the 2008 International Joint Conference on Neural Networks (IJCNN2008), Hong Kong, 2008, pp. 2526-2531.

X. Yu, K. Tang and X. Yao, “An Immigrants Scheme Based on Environmental Information for Genetic Algorithms in Changing Environments,” in Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), Hong Kong, 2008, pp. 1141-1147.

Z. Wang, K. Tang and X. Yao, “A Multi-objective Approach to Testing Resource Allocation in Modular Software Systems,” in Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), Hong Kong, 2008, pp. 1148-1153.

Z. Yang, K. Tang and X. Yao, “Multilevel Cooperative Coevolution for Large Scale Optimization,” in Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), Hong Kong, 2008, pp. 1663-1670.

Z. Yang, K. Tang and X. Yao, “Self-adaptive Differential Evolution with Neighborhood Search,” in Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), Hong Kong, 2008, pp. 1110-1116.

K. Tang, Z. Wang, X. Cao and J. Zhang, “A Multi-objective Evolutionary Approach to Aircraft Landing Scheduling Problems,” in Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), Hong Kong, 2008, pp. 3651-3657.

T. Chen, K. Tang, G. Chen and X. Yao, “On the Analysis of Average Time Complexity of Estimation of Distribution Algorithms,” in Proceedings of 2007 IEEE Congress on Evolutionary Computation (CEC2007), Singapore, 2007, pp. 453-460.

Z. Yang, K. Tang and X. Yao, “Differential Evolution for High-Dimensional Function Optimization,” in Proceedings of 2007 IEEE Congress on Evolutionary Computation (CEC2007), Singapore, 2007, pp. 3523-3530.

A. Ashish, G. Fogel, E. K. Tang and P. N. Suganthan, “Feature Selection Approach for Quantitative Prediction of Transcriptional Activities,” in Proceedings of the 2006 IEEE Symposium on Computational Intelligence in 生物信息学 and Computational Biology 2006.

E. K. Tang, P. N. Suganthan and X. Yao, “Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimization,” in Proceedings of the 2005 IEEE Symposium on Computational Intelligence in 生物信息学 and Computational Biology (CIBCB), San Diego, USA, November 2005, pp. 9-17.

E. K. Tang, P. N. Suganthan and X. Yao, “Nonlinear Feature Extraction Using Evolutionary Algorithm,” in Proceedings of the 11th Int. Conference on Neural Information Processing, Calcutta, India, November 2004, LNCS Vol. 3316, pp. 1014-1019.

E. K. Tang, P. N. Suganthan and X. Yao, “Generalized LDA Using Relevance Weighting and Evolution Strategy,” in Proceedings of the 2004 Congress on Evolutionary Computation, Portland, USA, June 2004, Vol. 2, pp. 2230-2234.

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参考资料

唐珂 - 教师个人主页 - 南方科技大学.南方科技大学.2021-11-13

2023 IEEE Fellow华人占三成，唐立新宗成庆等入选.今日头条.2022-11-30

学术成果.南方科技大学.2021-11-13

科研项目 - 唐珂 - 教师个人主页 - 南方科技大学.南方科技大学.2021-11-13