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周爱民

职称: 研究员

直属机构: 计算机科学与技术学院

学科:

10 访问

相关教师

个人资料

  • 部门: 计算机科学与技术学院
  • 性别:
  • 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,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

个人简介

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(2011-2021

研究方向

演化搜索与优化(Evolutionary Search and Optimization)

智能教育(AI for Education)

工业应用(Industry Applications)


开授课程

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

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,2012-2017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,2011-2015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=en

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

主要论文:

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

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

[3].H. Zhang, A. Zhou, and H. Zhang, An evolutionary forest for regression, IEEE Transactions on Evolutionary Computation, 2022. (accepted)

[4].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.

[5].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.

[6].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.

[7].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.

[8].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.

[9].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.

[10].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.

[11].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.

[12].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.

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

[14].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.

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

[16].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.

[17].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.

[18].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.

[19].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.

[20].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.

[21].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.

[22].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.

[23].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.

[24].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.

[25].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.

[26].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.

[27].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.

[28].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.

[29].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.

[30].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.

[31].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.

[32].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.

[33].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, 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, 导师: 康立山教授.

荣誉及奖励