导航 个人资料 个人概况 研究方向 开授课程 科研项目 学术成果 荣誉及奖励,周爱民 职称： 教授 直属机构： 计算机科学与技术学院, 上海智能教育研究院 学科： 40802 访问 相关教师 个人资料 部门： 计算机科学与技术学院, 上海智能教育研究院 性别： 男 专业技术职务： 教师 毕业院校： Essex大学 学位： 博士 学历： 研究生 联系电话： 021-62233040 电子邮箱： amzhou@cs.ecnu.edu.cn 办公地址： 理科大楼B503室 通讯地址： 上海市中山北路3663号 邮编： 200062 传真： 教育经历 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 工作经历 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 个人简介 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。 社会兼职 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） 研究方向 (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！ 开授课程 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，2021AIoT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，2016-2017Windows程序设计，本科选修，2012编程实践，本科必修，2010-2012 科研项目 [7] 数据驱动与知识引导的可解释性机器学习模型构建理论与方法，上海市科委人工智能专项，2019年-2022年，项目号：19511120600，主持人。[6] 面向数据的快速磁共振成像 ，自然科学基金重点项目，2018年-2022年，项目号：61731009，主要参与者。[5] 模型辅助演化多目标优化及应用，自然科学基金面上项目，2017年-2020年，项目号：61673180，主持人。[4] 基于学习技术的多目标进化算法重组算子研究，自然科学基金面上项目，2013年-2016年，项目号：61273313，主持人。[3] 便携式拉曼光谱仪研制，科技部重大仪器专项课题，2012年-2017年，项目号：2012YQ180132-01，子课题主持人。[2] 多源异质数据的信息提取与快速变化检测，科技部973计划项目课题，2011年-2015年，项目号：2011CB707104，主要参与者。[1] 求解多目标旅行商问题的分布估计算法研究，自然科学基金青年项目，2011年，项目号：44102330，主持人。 学术成果 Google Citation:http://scholar.google.com/citations?user=E4GQv5cAAAAJ&hl=enDBLP: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. 荣誉及奖励 [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.
