周爱民

2004.10-2009.06：英国Essex大学计算机与电子工程学院，获博士学位

2003.09-2004.09：武汉大学计算机学院，博士在读

2001.09-2003.06：武汉大学计算机学院，获硕士学位（提前毕业）

1997.09-2001.06：武汉大学计算机学院，获学士学位

2016.12-：华东师范大学，教授

2012.12-2016.12：华东师范大学，副教授

2009.06-2012.12：华东师范大学，讲师

担任华东师范大学计算机科学与技术学院院长、华东师范大学上海智能教育研究院院长。爱思唯尔2020-2022年度中国高被引学者。于2001年在武汉大学获得计算机学士学位、2003年在武汉大学获得计算机硕士学位、2009年在英国Essex大学获得计算机博士学位，2009年起在华东师范大学工作。主要研究领域包括演化优化与学习、可解释机器学习、智能教育、科学智能等。发表SCI一区/CCF A类期刊会议学术论文40余篇，相关成果谷歌学术累计引用8400余次。出版专著1本。申请或授权发明专利10余项。担任Swarm and Evolutionary Computation、Complex & Intelligent Systems、Chinese Journal of Electronics等期刊副编或编委。参与创办演化计算与优化(ECOLE)研讨会并担任2016年会议主席。

IEEE高级会员

中国计算机学会(CCF)会员

Swarm and Evolutionary Computation副编

Complex & Intelligent Systems编委

Chinese Journal of Electronics编委

IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Computational Intelligence Magazine, Pattern Recognition, Information Sciences, 软件学报, 计算机学报, CEC, GECCO, EMO, IJCAI, AAAI, NeurIPS等期刊和会议审稿人

国家自然科学基金通讯评审专家（2011-2023）

(1) 演化优化与学习

(2) 可解释机器学习

(3) 智能教育

(4) 科学智能

欢迎对上述方向感兴趣的本科生同学参与科研和双创活动，欢迎报考硕士和（计算机及智能教育）博士研究生，欢迎博士后、青年研究员等研究人员加盟计算机学院及智能教育研究院团队，请邮件联系！

• 计算机导论，本科必修，2021-2023

• 智能教育，本科师范生选修课，2022-2023

• 人工智能之路，研究生通识课，2022-2023

• 人工智能，本科必修，2010-2023

• 人工智能前沿专题，博士生必修，2021

• AIoT智能系统，研究生选修，2021

• 人工智能前沿，研究生必修，2018-2020

• 计算智能，研究生必修，2012-2016
  

• 最优化方法，研究生选修，2016-2017

• Windows程序设计，本科选修，2012

• 编程实践，本科必修，2010-2012

Google Citation:http://scholar.google.com/citations?user=E4GQv5cAAAAJ&hl=en

DBLP:https://dblp.uni-trier.de/pers/hd/z/Zhou:Aimin

主要论文：

[1] B. Li, Y. Zhang, P. Yang, X. Yao, and A. Zhou, A two-population algorithm for large-scale multi-objective optimization based on fitness-aware operator and adaptive environmental selection, IEEE Transactions on Evolutionary Computation, 2023. (Accept)

[2] S. Wang, A. Zhou, Regularity evolution for multiobjective optimization, IEEE Transactions on Evolutionary Computation, 2023. (Accept)

[3] Y. Lu, B. Li, S. Liu, and A. Zhou, A population cooperation based particle swarm optimization algorithm for large-scale multi-objective optimization, Swarm and Evolutionary Computation, 2023. (Accept)

[4] S. Wang, A. Zhou, G. Zhang, and F. Fang, Learning regularity for evolutionary multiobjective search: A generative model-based approach, IEEE Computational Intelligence Magazine, 2023. (Accept)

[5] H. Zhang, A. Zhou, Q. Chen, B. Xue, and M. Zhang, SR-Forest: A genetic programming based heterogeneous ensemble learning method, IEEE Transactions on Evolutionary Computation, 2023. (Accept)

[6] H. Qian, Y. Zeng, T. Wu, S. Liu, C. Zheng, and A. Zhou, Evolutionary Bayesian error attribution networks for fine-grained cognitive diagnosis in student learning, SCIENCE CHINA Information Sciences, 2023. (Accept)

[7] Z Wang, B Mao, H Hao, W Hong, C Xiao, A Zhou, Enhancing diversity by local subset selection in evolutionary multiobjective optimization, IEEE Transactions on Evolutionary Computation, 2022. (Accept)

[8] 钱鸿, 舒翔, 孙天祥, 邱锡鹏, 周爱民,基于动态批量评估的绿色无梯度优化方法研究, 软件学报, 2023. (已接收)

[9] 吴宇鹏, 钱鸿, 王为业, 张杨文辉, 周爱民,基于优先级先验的演化大规模多目标安全博弈框架, 计算机研究与发展, 2023. (已接收)

[10] M. Yang, J. Gao, A. Zhou, et al. Contribution-based cooperative co-evolution with adaptive population diversity for large-scale global optimization, IEEE Computational Intelligence Magazine, 18(3): 56-68, 2023.

[11] S. Wang, A. Zhou, B. Li, and P. Yang, Differential evolution guided by approximated Pareto set for multiobjective optimization, Information Sciences, 630: 669-687, 2023.

[12] S. Wang, B. Li, and A. Zhou, A regularity augmented evolutionary algorithm with dual-space search for multiobjective optimization, Swarm and Evolutionary Computation, 78: 101261, 2023.

[13] J Cui, Z Chen, A Zhou, J Wang, W Zhang, Fine-grained interaction modeling with multi-relational transformer for knowledge tracing, ACM Transactions on Information Systems, 41(4): 1-26, 2023.

[14] H. Wang, B. Li, S. Wu, S. Shen, F. Liu, S. Ding, and A Zhou, Rethinking the learning paradigm for dynamic facial expression recognition, in CVPR, 17958-17968, 2023.

[15] 金天成, 窦亮, 肖春芸, 张伟, 周爱民, 记忆与认知融合的个性化OJ习题推荐, 计算机学报, 46(1):103-124, 2023.

[16] H. Hao, A. Zhou, H. Qian, and H. Zhang, Expensive multiobjective optimization by relation learning and prediction, IEEE Transactions on Evolutionary Computation, 26(5): 1157-1170, 2023.

[17] W. Zhang, S. Wang, A. Zhou, and H. Zhang, A practical regularity model based evolutionary algorithm for multiobjective optimization, Applied Soft Computing, 129: 109614, 2022.

[18] H. Zhang, A. Zhou, H. Qian, and H. Zhang, PS-Tree: A piecewise symbolic regression tree, Swarm and Evolutionary Computation, 71, 101061, 2022.

[19] H. Zhang, A. Zhou, and H. Zhang, An evolutionary forest for regression, IEEE Transactions on Evolutionary Computation, 26(4):735-749, 2022.

[20] Y. Qian, X. Li, J. Wu, A. Zhou, Z. Xu, and Q. Zhang, Picture-word order compound protein interaction: Predicting compound-protein interaction using structural images of compounds, Journal of Computational Chemistry, 43(4):255-264, 2022.

[21] Y. Chen, A. Zhou, and S. Das, Utilizing dependence among variables in evolutionary algorithms for mixed-integer programming: A case study on multi-objective constrained portfolio optimization, Swarm and Evolutionary Computation, 66(2021) 100928, 2021.

[22] F. Wang, H. Zhang, and A. Zhou, A particle swarm optimization algorithm for mixed-variable optimization problems, Swarm and Evolutionary Computation, 60(2021)100808, 2021.

[23] C. Liu, T. Bian, and A. Zhou, Multiobjective multiple features fusion: A case study in image segmentation, Swarm and Evolutionary Computation, 60(2021)100792, 2021.

[24] M. Yang, A. Zhou, X. Yao, and C. Li, An efficient recursive differential grouping for large-scale continuous problems, IEEE Transactions on Evolutionary Computation, 25(1):159-171, 2021.

[25] H. Hao, J. Zhang, X. Lu, and A. Zhou, Binary relation learning and classifying for preselection in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 24(6):1125-1139, 2020.

[26] 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, 2020.

[27] 张晋媛,周爱民,张桂戌,演化算法中一种基于单分类的预选择策略,计算机学报, 43(2):233-249, 2020.

[28] M. Yang, A. Zhou, C. Li, J. Guan, and X. Yan, CCFR2: A more efficient cooperative co-evolutionary framework for large-scale global optimization, Information Sciences, 512:64-79, 2020.

[29] A. Zhou, Y. Wang, and J. Zhang, Objective extraction via Fuzzy clustering in evolutionary many-objective optimization, Information Sciences, 509:343-355, 2020.

[30] 陈晓纪,石川,周爱民,吴斌,一种基于混合个体选择机制的多目标进化算法,软件学报, 30(12):3651-3664, 2019.

[31] A. Zhou, J. Zhang, J. Sun, and G. Zhang, Fuzzy-classification assisted solution preselection in evolutionary optimization, in AAAI, pp. 2403-2410, 2019.

[32] J. Sun, H. Zhang, A. Zhou, Q. Zhang, K. Zhang, Z. Tu, and K. Ye, Learning from a stream of nonstationary and dependent data in multiobjective evolutionary optimization, IEEE Transactions on Evolutionary Computation, 23(4):541-555, 2019.

[33] 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, 2019.

[34] [J. Sun, H. Zhang, A. Zhou, Q. Zhang, and K. Zhang, A new learning-based adaptive multi-objective evolutionary algorithm, Swarm and Evolutionary Computation, 44:304-319, 2019.

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

[36] H. Fang, A. Zhou, and H. Zhang, Information fusion in offspring generation: A case study in DE and EDA, Swarm and Evolutionary Computation, 42:99-108, 2018.

[37] J. Sun, A. Zhou, S. Keates, and S. Liao, Simultaneous Bayesian clustering and feature selection through student’s t mixtures model, IEEE Transactions on Neural Networks and Learning Systems, 29(4):1187-1199, 2018.

[38] H. Zhang, A. Zhou, S. Song, Q. Zhang, X. Gao, and J. Zhang, A self-organizing multiobjective evolutionary algorithm, IEEE Transactions on Evolutionary Computation, 20(5):792-806, 2016.

[39] L. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, Constrained subproblems in decomposition based multiobjective evolutionary algorithm, IEEE Transactions on Evolutionary Computation, 20(3):475-480, 2016.

[40] A. Zhou, and Q. Zhang, Are all the subproblems equally important? Resource allocation in decomposition based multiobjective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 20(1):52-64, 2016.

[41] [28] Z. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, Adaptive replacement strategies for MOEA/D, IEEE Transactions on Cybernetics, 46 (2):474-486, 2016.

[42] A. Zhou, J. Sun, and Q. Zhang, An estimation of distribution algorithm with cheap and expensive local search, IEEE Transactions on Evolutionary Computation, 19 (6): 807-822, 2015.

[43] W. Gong, A. Zhou, and Z. Cai, A multi-operator search strategy based on cheap surrogate models for evolutionary optimization, IEEE Transactions on Evolutionary Computation, 19 (5): 746-758, 2015.

[44] A. Zhou, Y. Jin, and Q. Zhang, A population prediction strategy for evolutionary dynamic multiobjective optimization, IEEE Transactions on Cybernetics, 44(1):40-53,2014.

[45] 周爱民,张青富,张桂戌,一种基于混合高斯模型的多目标进化算法,软件学报, 5:913-928, 2014.

[46] A. Zhou, B. Qu, H. Li, S. Zhao, P. Suganthan, and Q. Zhang, Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, 1(1): 32–49, 2011.

[47] A. Zhou, Q. Zhang and Y. Jin, Approximating the set of Pareto optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation, 13(5):1167-1189, 2009.

[48] Q. Zhang, A. Zhou, and Y. Jin, RM-MEDA: A regularity model based multiobjective estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation, 12(1):41-63, 2008.

学位论文：

[1] 博士论文: Estimation of distribution algorithms for continuous multiobjective optimization, University of Essex, 2009年, 导师: Qingfu Zhang教授, Edward Tsang教授, Yaochu Jin教授(Honda Research Institute Europe), Bernhard Sendhoff博士(Honda Research Institute Europe).

[2] 硕士论文: 演化建模及其应用, 武汉大学, 2003年, 导师: 康立山教授.

[1]     2023, 吴宇鹏, 第十九届中国机器学习会议（CCML）最佳学生论文奖.

[2]     2023, 李文浩, 上海市计算机学会优秀博士论文提名奖.

[3]     2022, 李明嘉, 江苏省人工智能学术会议, 优秀学生论文奖.

[4]     2022, 张恒哲, 上海市优秀毕业生.

[5]     2021, 郝昊, NeurIPS 2021 competition Machine Learning for Combinatorial Optimization (ML4CO), 第一名.

[6]     2021, 高思宇, 上海市优秀毕业生.

[7]     2020, 高思宇, 华东师范大学校长奖学金(本科生).

[8]     2020, 张恒哲, 第三届众安大学生黑客马拉松大赛冠军.

[9]     2020, 吴婷, “华为杯”第十七届中国研究生数学建模竞赛二等奖.

[10]  2020, 张恒哲, “华为杯”第十七届中国研究生数学建模竞赛二等奖.

[11]  2018, 赵昊颖, “华为杯”第十五届中国研究生数学建模竞赛二等奖.

[12]  2017, 赵树锴, “华为杯”第十四届中国研究生数学建模竞赛二等奖.