刘静，西安电子科技大学广州研究院副院长、二级教授、博士生导师。分别于2000年与2004年在西安电子科技大学获得学士与博士学位，2009年破格晋升为教授。期间于2007年4月至2008年4月在澳大利亚昆士兰大学做博士后、2009年7月至2011年7月在澳大利亚新南威尔士大学国防研究院做研究员。

长期从事智能优化，复杂网络系统认知、预测与调控，时间序列分析，计算机视觉领域的研究工作，已合作出版专著4部、发表国际期刊论文110余篇、国际会议论文70余篇。2015-2020任人工智能领域顶级期刊《IEEE Trans. Evolutionary Computation》副编，2017-2018任IEEE智能计算学会涌现科技技术委员会主席。

已主持多项国家级、省部级科研项目。2013年作为第三完成人获得国家自然科学二等奖，2014年获得吴文俊人工智能科学技术创新二等奖（个人奖），2015年入选国家级青年人才，2018年入选国家级人才，同年被批准为享受国务院特殊津贴专家。

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招生说明（2024.4.7日更新）：

1. 本人选择博士生、硕士生的条件是要有积极主动进取的学习态度，能全身心地投入到科研工作中，具有责任心，以追求卓越的科研成果为目标，对学生的本科毕业院校、专业背景没有要求；

2. 我目前全职在我校广州研究院工作，博士生、硕士生在本部修完主要课程后需在广州进行科研实践培养，对此不能接受的请选择其他导师；

3. 2025年度预计有1~2个工学博士招生指标（必须脱产全日制攻读）、1~2个工程博士招生指标（可以在职攻读），感兴趣的同学请先评估自己满足以上两个条件再联系我。目前阶段我不会承诺给名额，只有等正式报名后我会根据报名情况择优录取；

4. 有意保送或报考我研究生的同学请先发简历至我的邮箱neouma@mail.xidian.edu.cn！由于工作繁忙，不能一一回复邮件，我仅给我初选合格的学生回复邮件以确定进一步的事宜（一般会在3天内回复），望谅解！

5. 我团队教师的学生统一培养，对我团队感兴趣的同学也可以联系我团队的教师：刘晓涛、赵宏、黄婷、滕祥意、吴凯。

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专著

1.Jing Liu, Hussein A. Abbass, and Kay Chen Tan, Evolutionary Computation and Complex Networks, Springer, 2018.

2.David G. Green, Jing Liu, and Hussein A. Abbass, Dual Phase Evolution, Springer, 2013.

3.Licheng Jiao, Jing Liu, and Weicai Zhong, Coevolutionary Computation and Multiagent Systems, WIT, U. K., 2012.

4.焦李成，刘静，钟伟才，协同进化计算与多智能体系统，科学出版社，2006.

指导研究生发表的国际期刊论文

（第一作者均为我的博士生或硕士生）

智能优化（按发表年份倒序排列）

5.Chao Wang, Jing Liu, Kai Wu, and Zhaoyang, Wu, Solving multi-task optimization problems with adaptive knowledge transfer via anomaly detection, IEEE Transactions on Evolutionary Computation, 26(2): 304-318, 2022.

6.Xingxing Hao, Rong Qu, and Jing Liu, A unified framework of graph-based evolutionary multitasking hyper-heuristic, IEEETransactionsEvolutionary Computation, 25(1): 35-47, 2021.

7.Baihao Qiao, Jing Liu, and Xingxing Hao, A multi-objective differential evolution algorithm and a constraint handling mechanism based on variables proportion for dynamic economic emission dispatch problems, Applied Soft Computing Journal, 108: 107419, 2021.

8.Yating Cao, Jing Liu, and Zhouwu Xu, A hybrid particle swarm optimization algorithm for RFID network planning, Soft Computing, 25: 5747-5761, 2021.

9.Xingxing Hao, Jing Liu, Mathematical model and simulated annealing algorithm for Chinese high school timetabling problems under the new curriculum innovation, Frontiers of Computer Science, 15, 151309, 2021.

10.Baihao Qiao and Jing Liu,Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm, Renewable Energy, 154: 316-336, 2020.

11.Xingxing Hao, Jing Liu, Xiaoxiao Yuan, Xianglong Tang, Zhangtao Li. A moving block sequence-based evolutionary algorithm for resource-constrained project scheduling problems. International Journal of Bio-Inspired Computation, 14(2): 85-102, 2019.

12.Tao Zhang, Jing Liu, An efficient and fast kinematics-based algorithm for RFID network planning, Computer Networks, 121: 13-24, 2017.

13.Penghui Liu, Jing Liu, Multi-leader PSO (MLPSO): a new PSO variant for solving global optimization problems, Applied Soft Computing, 61: 256-263, 2017.

14.Yawen Zhou, Jing Liu, and Xiaohui Gan, A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems, Transportation Research Part E, 99: 77-95, 2017.

15.Xingxing Hao and Jing Liu, A multiagent evolutionary algorithm with direct and indirect combined representation for constraint satisfaction problems, Soft Computing, 21(3): 781-793, 2017.

16.Xiaoxiao Yuan, Jing Liu, and Xingxing Hao, A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems,Big Data and Information Analytics, 2(1): 39-58, 2017.

17.Yutong Zhang, Jing Liu, Mingxing Zhou, and Zhongzhou Jiang, A multi-objective memetic algorithm based on decomposition for big optimization problems, Memetic Computing, 8(1): 45-61, 2016.

复杂网络（按发表年份倒序排列）

18.Kai Wu, Xingxing Hao, and Jing Liu, Online reconstruction of complex networks from streaming data, IEEE Transactions on Cybernetics, 52(6): 5136-5147, 2022.

19.Xiangyi Teng, Jing Liu, and Liqiang Li, A synchronous feature learning method for multiplex network embedding, Information Sciences, 574: 176-191, 2021.

20.Fang Shen, Jing Liu, and Kai Wu, Evolutionary multitasking network reconstruction from time series with online parameter estimation, Knowledge-Based Systems, 222, 107019, 2021.

21.Shuai Wang, Jing Liu, and Yaochu Jin, A computationally efficient evolutionary algorithm for multi-objective network robustness optimization, IEEE Trans. Evolutionary Computation, 25(3): 419-432, 2021.

22.Kai Wu, Jing Liu, Xingxing Hao, Penghui Liu, and Fang Shen, An evolutionary multi-objective framework for complex network reconstruction using community structure, IEEE Transactions on Evolutionary Computation, 25(2): 247-261, 2021.

23.Shuai Wang, Jing Liu, Yaochu Jin, Finding influential nodes in multiplex networks using a memetic algorithm, IEEE Trans. Cybernetics, 51(2): 900-912, 2021.

24.Xiangyi Teng, Jing Liu, Mingming Li, Overlapping community detection in directed and undirected attributed networks using a multiobjective evolutionary algorithm, IEEE Trans. Cybernetics, 51(1): 138-150, 2021.

25.Shuai Wang, Jing Liu, and Yaochu Jin, Surrogate-assisted robust optimization of large-scale networks based on graph embedding, IEEE Trans. Evolutionary Computation, 24(4): 735-749, August, 2020.

26.Mingming Li, Jing Liu, Peng Wu, and Xiangyi Teng, Evolutionary network embedding preserving both local proximity and community structure, IEEE Transactions on Evolutionary Computation, 24(3): 523-535, 2020.

27.Shuai Wang, Jing Liu, Yaochu Jin, Robust structural balance in signed networks using a multi-objective evolutionary algorithm, IEEE Computational Intelligence Magazine, pp. 24-35, May, 2020.

28.Luowen Liu and Jing Liu, Reconstructing gene regulatory networks via memetic algorithm and LASSO based on recurrent neural networks, Soft Computing, 24(6): 4205-4221, 2020.

29.Liqiang Li and Jing Liu, The aggregation of multiplex networks based on the similarity of networks, Physica A, 540, 122976, 2020.

30.Shuai Wang and Jing Liu, Community robustness and its enhancement in interdependent networks, Applied Soft Computing, 77: 665-677, 2019.

31.Shuai Wang and Jing Liu, Constructing robust community structure against edge-based attacks, IEEE Systems Journal, 13(1): 582-592, 2019.

32.Kai Wu, Jing Liu, and Dan Chen, Network reconstruction based on time series via memetic algorithm, Knowledge-Based Systems, 164: 404-425, 2019.

33.Shuai Wang, Jing Liu, Designing comprehensively robust networks against intentional attacks and cascading failures, Information Sciences, 478, 125-140, 2019.

34.Yonglei Lu and Jing Liu. The impact of information dissemination strategies to epidemic spreading on complex networks. Physica A: Statistical Mechanics and its Applications, vol. 536, 120920, 2019.

35.Penghui Liu and Jing Liu, Good influence transmission structure strengthens cooperation in prisoner’s dilemma games, European Physical Journal B, 91: 321: 1-12, 2018.

36.Zhirou Yang and Jing Liu, A memetic algorithm for determining the nodal attacks with minimum cost on complex networks, Physica A, 503, 1041-1053, 2018.

37.Xiaodong Wang and Jing Liu, A comparative study of the measures for evaluating community structure in bipartite networks, Information Sciences, 448-449: 249-262, 2018.

38.Lei Rong and Jing Liu, A heuristic algorithm for enhancing the robustness of scale-free networks based on edge classification,Physica A: Statistical Mechanics and its Applications, 503:503-515, 2018.

39.Mingming Li and Jing Liu, A link clustering based memetic algorithm for overlapping community detection,Physica A: Statistical Mechanics and its Applications, 503: 410-423, 2018.

40.Zhangtao Li, Jing Liu, and Kai Wu, A multi-objective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks, IEEE Trans. Cybernetics, 48(7): 1963-1976, JULY 2018.

41.Shuai Wang and Jing Liu, A multi-objective evolutionary algorithm for promoting the emergence of cooperation and controllable robustness on directed networks, IEEE Transactions on Network Science and Engineering, 5(2): 92-100, 2018.

42.Zhirou Yang and Jing Liu, Robustness of scale-free networks with various parameters against cascading failures, Physica A: Statistical Mechanics and its Applications, 492: 628-638, 2018.

43.Shuai Wang, Jing Liu, and Xiaodong Wang, Mitigation of attacks and errors on community structure in complex networks, Journal of Statistical Mechanics Theory and Experiment, 4, 043405, 2017.

44.Jing Liu, Mingxing Zhou, Shuai Wang, and Penghui Liu, A comparative study of network robustness measures, Frontiers of Computer Science, 11(1): 1-17, 2017.

45.Penghui Liu and Jing Liu, Multilevel evolutionary algorithm that optimizes the structure of scale-free networks for the promotion of cooperation in the prisoner’s dilemma game, Scientific Reports, 7, 4320, 2017.

46.Penghui Liu and Jing Liu, Contribution diversity and incremental learning promote cooperation in public goods games, Physica A: Statistical Mechanics and its Applications, 486: 827-838, 2017.

47.Penghui Liu and Jing Liu, Robustness of coevolution in resolving prisoner’s dilemma games on interdependent networks subject to attack, Physica A: Statistical Mechanics and its Applications, 479: 362-370, 2017.

48.Mingxing Zhou and Jing Liu, A two-phase multi-objective evolutionary algorithm for enhancing the robustness of scale-free networks against multiple malicious attacks, 47(2): 539-552, IEEETransactions on Cybernetics, 2017.

49.Shuai Wang and Jing Liu, Constructing robust cooperative networks using a multi-objective evolutionary algorithm, Scientific Reports, 7, 41600, 2017.

50.Penghui Liu and Jing Liu, Cooperation in the prisoner’s dilemma game on tunable community networks, Physica A: Statistical Mechanics and its Applications, 472: 152-163, 2017.

51.Xiaodong Wang and Jing Liu, A layer reduction based community detection algorithm on multiplex networks, Physica A: Statistical Mechanics and its Applications, 471: 244-252, 2017.

52.Jinhuang Huang, Jing Liu, and Xin Yao, A multi-agent evolutionary algorithm for software module clustering problems, Soft Computing, 21(12): 3415-3428, 2017.

53.Mingxing Zhou, Jing Liu, Shuai Wang, and Shan He, A comparative study of robustness measures for cancer signaling networks, Big Data and Information Analytics, 2(1): 87-96, 2017.

54.Xianglong Tang, Jing Liu, and XingxingHao, Mitigate cascading failures on networks using a memetic algorithm, Scientific Reports, 6, 38713, 2016.

55.Kai Wu, Jing Liu, and Shuai Wang, Reconstructing networks from profit sequences in evolutionary games via a multiobjective optimization approach with lasso initialization, Scientific Reports,6, 37771,2016.

56.Zhongzhou Jiang, Jing Liu, and Shuai Wang, Traveling salesman problems with PageRank distance on complex networks reveal community structure, Physica A: Statistical Mechanics and its Applications, 463: 293-302, 2016.

57.Shuai Wang and Jing Liu, The effect of link-based topological changes and recoveries on the robustness of cooperation on scale-free networks, The European Physical Journal Plus, 131: 219, 2016.

58.Shuai Wang and Jing Liu, Robustness of single and interdependent scale-free interaction networks with various parameters, Physica A: Statistical Mechanics and its Applications, 460: 139-151, 2016.

59.Boping Duan, Jing Liu, and Xianglong Tang, Optimizing the natural connectivity of Scale-free networks using simulated annealing, Physica A: Statistical Mechanics and its Applications, 457: 192-201, 2016.

60.Liangliang Ma, Jing Liu, and Boping Duan, Evolution of network robustness under continuous topological changes, Physica A: Statistical Mechanics and its Applications, 451: 623-631, 2016.

61.Jinhuang Huang and Jing Liu, A similarity-based modularization quality measure for software module clustering problems, Information Sciences, 342: 96-110, 2016.

62.Chenlong Liu, Jing Liu, and Zhongzhou Jiang, An improved multi-objective evolutionary algorithm for simultaneously detecting separated and overlapping communities, Natural Computing, 15(4): 635-651, 2016.

63.Zhangtao Li and Jing Liu, A multi-agent genetic algorithm for community detection in complex networks, Physica A: Statistical Mechanics and its Applications, 449: 336-347, 2016.

64.Boping Duan, Jing Liu, Mingxing Zhou, and Liangliang Ma, A comparative analysis of network robustness against different link attacks, Physica A: Statistical Mechanics and its Applications, 448: 144-153, 2016.

65.Xianglong Tang, Jing Liu, and Mingxing Zhou, Enhancing network robustness against targeted and random attacks using a memetic algorithm, EPL (Europhysics Letters),111(3): 38005-p1-p6, 2015.

66.Liangliang Ma, Jing Liu, Boping Duan, and Mingxing Zhou, A theoretical estimation for the optimal network robustness measure R against malicious node attacks, EPL (Europhysics Letters), 111(2): 28003-p1-p5, 2015.

67.Mingxing Zhou and Jing Liu, A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks, Physica A: Statistical Mechanics and its Applications, 410: 131-143, 2014.

68.Chenlong Liu, Jing Liu, Zhongzhou Jiang, A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks, IEEE Transactions on Cybernetics, 44(12): 2274-2287, 2014.

69.Yadong Li, Jing Liu, and Chenlong Liu,A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks, Soft Computing, 18(2): 329-348, 2014.

模糊认知图学习与时间序列分析（按发表年份倒序排列）

70.Kai Wu, Jing Liu, Penghui Liu, and Fang Shen, Online fuzzy cognitive map learning, IEEE Trans. Fuzzy Systems, 29(7): 1885-1898, 2021.

71.Fang Shen, Jing Liu, and Kai Wu, Multivariate time series forecasting based on elastic net and high-order fuzzy cognitive maps: a case study on human action prediction through EEG signals, IEEE Trans. Fuzzy Systems, 29(8): 2336-2348, 2021.

72.Chao Wang, Jing Liu, Kai Wu, and Chaolong Ying, Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm, Applied Soft Computing, 108: 107441, 2021.

73.Kai Wu, Jing Liu, Penghui Liu, and Shanchao Yang, Time series prediction using sparse autoencoder and high-order fuzzy cognitive maps, IEEE Trans. Fuzzy Systems, 28(12): 3110-3121, Dec. 2020.

74.Kaixin Yuan, Jing Liu, Shanchao Yang, Kai Wu, and Fang Shen, Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps, Knowledge-Based Systems, vol. 206, p. 106359, 2020.

75.Zongdong Liu and Jing Liu, A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps, Knowledge-Based Systems, 203: 106105, 2020.

76.Penghui Liu, Jing Liu, and Kai Wu, CNN-FCM: system modeling promotes stability of deep learning in time series prediction, Knowledge-Based Systems, 203: 106081, 2020.

77.Fang Shen, Jing Liu, and Kai Wu, A preference-based evolutionary biobjective approach for learning large-scale fuzzy cognitive maps: an application to gene regulatory network reconstruction, IEEE Trans. Fuzzy Systems, 28(6): 1035-1049, 2020.

78.Fang Shen, Jing Liu, and Kai Wu, Evolutionary multitasking fuzzy cognitive map learning, Knowledge-Based Systems, 192: 105294, 2020.

79.Ze Yang and Jing Liu, Learning fuzzy cognitive maps with convergence using a multi-agent genetic algorithm, Soft Computing, 24(6): 4055-4066, 2020.

80.Luowen Liu and Jing Liu, A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps, Journal of Bioinformatics and Computational Biology, Vol.17, No.4, 1950023(25 pages), 2019.

81.Ze Yang and Jing Liu, Learning of fuzzy cognitive maps using a niching-based multi-modal multi-agent genetic algorithm, Applied Soft Computing, 74, 356-367, 2019.

82.Jing Liu, Yaxiong Chi, Zongdong Liu, and Shan He, Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps, CAAI Transactions on Intelligence Technology, 4(1): 24-36, 2019.

83.Yaxiong Chi and Jing Liu. Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps. Natural Computing, vol.18, no.2, pp.301-312, 2019.

84.Yilan Wang and Jing Liu. A stable, unified density controlled memetic algorithm for gene regulatory network reconstruction based on sparse fuzzy cognitive maps. Neural Processing Letters, vol. 50, issue 3, pp.2843-2870, 2019.

85.Luowen Liu and Jing Liu, Inferring gene regulatory networks with hybrid of multi-agent genetic algorithm and random forests based on fuzzy cognitive maps, Applied Soft Computing, 69: 585-598, 2018.

86.Shanchao Yang and Jing Liu, Time series forecasting based on high-order fuzzy cognitive maps and wavelet transform, IEEE Trans. Fuzzy Systems, 26(6): 3391-3402, 2018.

87.Xumiao Zou and Jing Liu, A mutual information based two-phase memetic algorithm for large-scale fuzzy cognitive map learning, IEEE Trans. Fuzzy Systems, 26(4), pp. 2120-2134, Aug. 2018.

88.Kai Wu and Jing Liu, Learning large-scale fuzzy cognitive maps based on compressed sensing and application in reconstructing gene regulatory networks, IEEE Transactions on Fuzzy Systems, 25(6): 1546-1560, 2017.

89.Jing Liu, Yaxiong Chi, Chen Zhu, and Yaochu Jin, A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps, BMC Bioinformatics, 18: 241, 2017.

90.Kai Wu, Jing Liu, and Yaxiong Chi,Waveletfuzzycognitivemaps, Neurocomputing, 232: 94-103, 2017.

91.Kai Wu and Jing Liu, Robust learning of large-scale fuzzy cognitive maps via the lasso from noisy time series, Knowledge-Based Systems, 113: 23-38, 2016.

92.Jing Liu, Yaxiong Chi, and Chen Zhu, A dynamic multi-agent genetic algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps, IEEE Transactions on Fuzzy Systems, 24(2): 419-431, 2016.

93.Yaxiong Chi and Jing Liu, Learning of fuzzy cognitive maps with varying densities using a multi-objective evolutionary algorithm, IEEETransactions on Fuzzy Systems,24(1): 71-81, 2016.

计算机视觉（按发表年份倒序排列）

94.Fangtao Shao, Jing Liu, Peng Wu, Zhiwei Yang, and Zhaoyang Wu, Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images, Pattern Recognition, 122, 108261, 2022.

95.Peng Wu and Jing Liu, Learning causal temporal relation and feature discrimination for anomaly detection, IEEE Transactions Image Processing, vol. 30, pp.3513-3527, 2021.

96.Kuikui He, Xiaotao Liu, Jing Liu, and Peng Wu，A multitask learning-based neural network for defect detection on textured surfaces under weak supervision, IEEE Transactions on Instrumentation and Measurement, 70: 5016914, 2021.

97.Peng Wu and Jing Liu, Mingming Li, Yujia Sun, and Fang Shen, Fast sparse coding networks for anomaly detection in videos, Pattern Recognition, 107: 107515, 2020.

98.Peng Wu, Jing Liu, and Fang Shen, A deep one-class neural network for anomalous event detection in complex scenes, IEEE Transactions on Neural Networks and Learning Systems, 31(7): 2609-2622, 2020.

99.Xingxing Hao, Hui Zhao, and Jing Liu, Multifocus color image sequence fusion based on mean shift segmentation, Applied Optics, 54(30): 8982-8989, 2015.