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，研究员

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副院长（主持工作）、华东师范大学上海智能教育研究院副院长。爱思唯尔2020、2021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。主要研究领域包括机器学习、智能教育、AI for Science等。发表SCI一区/CCF A类期刊会议学术论文30余篇，相关成果谷歌学术累计引用6900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（2011-2022）

(1) 机器学习(纵向、横向课题支持)

    1.1 演化搜索与优化

    1.2 可解释性建模

    1.3 因果学习

(2) 智能教育(教育部华东师范大学智能教育实验室(培育)、上海市教委IV类高峰学科支持)

    2.1 可计算情感(计算机、心理、教育交叉方向)

    2.2 人机交互(计算机、教育交叉方向)

    2.3 自适应学习(计算机、教育交叉方向)

(3) AI for Science(上海市分子智造前沿科学研究基地支持)

    3. 1 智能分子合成(计算机、化学交叉方向)

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，2022

• 智能教育，本科师范生选修课，2022

• 人工智能之路，研究生通识课，2022

• 人工智能，本科必修，2010-2022

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

[2] H. Qian, X. Liu, C. Su, A. Zhou, and Y. Yu. The teaching dimension of regularized kernel learners, in ICML, 2022. (accepted)

[3] 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,2022.(已接收)

[4] H. Zhang, A. Zhou, H. Qian, and H. Zhang, PS-Tree: A piecewise symbolic regression tree, Swarm and Evolutionary Computation, 2022. (accepted)

[5] H. Hao, A. Zhou, H. Qian, and H. Zhang, Expensive multiobjective optimization by relation learning and prediction, IEEE Transactions on Evolutionary Computation, 2022. (accepted)

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

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

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

[17] 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.

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

[19] 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.

[20] 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.

[21] 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.

[22] 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.

[23] 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.

[24] 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.

[25] 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.

[26] 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.

[27] 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.

[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.

[29] 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.

[30] 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.

[31] 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.

[32] 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.

[33] A. Zhou, F. Gao, and G. Zhang, A decomposition based estimation of distribution algorithm for multiobjective traveling salesman problems, Computers and Mathematics with Applications, 66:1857–1868, 2013.

[34] 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.

[35] 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.

[36] 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] 2022, 张恒哲, 上海市优秀毕业生.

[2] 2021, 郝昊, NeurIPS 2021 competition Machine Learning for Combinatorial Optimization (ML4CO), 第一名.

[3] 2021, 高思宇, 上海市优秀毕业生.

[4] 2020, 高思宇, 华东师范大学校长奖学金(本科生).

[5] 2020, 张恒哲, 第三届众安大学生黑客马拉松大赛冠军.

[6] 2020, 吴婷, “华为杯”第十七届中国研究生数学建模竞赛二等奖.

[7] 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.

[8] 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.

[9] 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.