周爱民 职称： 研究员
直属机构： 计算机科学与技术学院
学科：





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相关教师




个人资料
部门： 计算机科学与技术学院
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：
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
4. 2004.10-2009.06：英国Essex大学计算与电子工程学院，获博士学位
3. 2003.09-2004.09：武汉大学计算机学院，博士在读
2. 2001.09-2003.06：武汉大学计算机学院，获硕士学位（提前毕业）
1. 1997.09-2001.06：武汉大学计算机学院，获学士学位

工作经历
3. 2016.12-：华东师范大学，研究员
2. 2012.12-2016.12：华东师范大学，副教授
1. 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
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（2011-2019）


研究方向
4. 演化搜索与最优化(Evolutionary Search and Optimization)
3. 机器学习(Machine Learning)
2. 图像处理(Image Processing)
1. 工业应用(Industry Applications)



开授课程
6. 人工智能，本科必修，2010-2020
5. 人工智能前沿，研究生必修，2018-2020
4. 计算智能，研究生必修，2012-2016
3. 最优化方法，研究生选修，2016-2017
2. Windows程序设计，本科选修，2012
1. 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：**，主持人。


6. 面向大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：**，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：**，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：**，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：**，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：
[38]H. Hao, J. Zhang, X. Lu, and A. Zhou, Binary relation learning and classifying for preselection in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 2020. (accepted)
[37]F. Wang, Y. Li, A. Zhou, and K. Tang, An estimation of distribution algorithm for mixed-variable Newsvendor problems, IEEE Transactions on Evolutionary Computation, 2019. (accepted)
[36]X. Chen, C. Shi, A. Zhou, and B. Wu, A multiobjective evolutionary algorithm based on hybridindividual selection mechanism,Journal of Software, 2018. (accepted,in Chinese)
[35]A. Zhou, Y. Wang, and J. Zhang, Objective extraction via Fuzzy clustering in evolutionary many-objective optimization,Information Sciences, 509:343-355, 2020.
[34]J. Zhang, A. Zhou, and G. Zhang, A pre-selection based on one-class classification in evolutionary algorithms, Chinese Journal of Computers,43(2):233-249, 2020.
[33]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.
[32]A. Zhou, J. Zhang, J. Sun, and G. Zhang, Fuzzy-classification assisted solution preselectionin evolutionary optimization, inAAAI,pp. 2403-2410, 2019.
[31]W. Hong, K.Tang, A. Zhou, H. Ishibuchi, and X. Yao, A scalable indicator-based evolutionaryalgorithm for large-scale multi-objective optimization,IEEE Transactions on Evolutionary Computation,23(3):525-537, 2019.
[30]J. Sun, H. Zhang, A. Zhou, Q. Zhang, and K. Zhang, A new learning-based adaptivemulti-objective evolutionary algorithm,Swarm and Evolutionary Computation,44:304-319, 2019.
[29]H.Zhang, and A. Zhou, Tree-structured decomposition and adaptation in MOEA/D, inParallel Problem Solving From Nature (PPSN XV), pp.359-371, 2018.
[28]J. Zhang, A. Zhou, K. Tang, and G. Zhang, Preselectionvia classification: A case study on evolutionary multiobjective optimization,Information Sciences, 465:388-403, 2018.
[27]D. Ding, Q. Zhang, L. Yang, A. Zhou, and J. Xia, Wiggly parallel-coupled line design by usingmultiobjective evolutionay algorithm,IEEE Microwave and Wireless Components Letters, 28(8):648-650, 2018.
[26]J. Zhang, A. Zhou, and G. Zhang, Preselection via classification: a case study on global optimization,International Journal of Bio-Inspired Computation, 11(4):257-266, 2018.
[25]H. Fang, A. Zhou, and H. Zhang, Information fusion in offspring generation: A case studyin DE and EDA,Swarm and Evolutionary Computation,42:92-108, 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.
[23]J. Zhang, A. Zhou, G. Zhang, and H. Zhang, A clustering based mate selection for evolutionary optimization,Big Data and Information Analytics, 2(1):77-85, 2017.
[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.
[21]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.
[20]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.
[19]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.
[18]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.
[17]Y. Xiao, F. Fang, Q. Zhang, A. Zhou, and G. Zhang, Parameter selection for variational Pan-sharpening by using evolutionary algorithm,Remote Sensing Letters,6(6):458-467, 2015.
[16]C. Liu, A. Zhou, C. Wu, and G. Zhang, Image segmentation framework based on multiple feature spaces,IET Image Processing, 9(4):271-279, 2015.
[15]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.
[14]G. Zhang, F. Fang, A. Zhou, and F. Li, Pan-sharpening of multi-spectral images using a new variational ?model,International Journal of Remote Sensing, 9(4):271-279, 2015.
[13]C. Li, A. Zhou, G. Zhang, and F. Fang, An antinoise method for hyperspectral unmixing,IEEE Geoscience and Remote Sensing Letters, 12(3):636-640, 2015.
[12]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.
[11]A. Zhou, Q. Zhang, and G. Zhang, A multiobjective evolutionary algorithm based on mixture Gaussian models,Journal of Software, 5:913-928, 2014. (in Chinese)
[10]C. Liu, A. Zhou, Q. Zhang, and G. Zhang, Adaptive image segmentation by using mean-shift and evolutionary optimization,IET Image Processing, 8(6):327-333, 2014.
[9]C. Li, F. Fang, A. Zhou, and G. Zhang, A novel blind spectral unmixing method based on error analysis of linear mixture model,IEEE Geoscience and Remote Sensing Letters, 11(7):1180-1184, 2014.
[8]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.
[7]C. Liu, A. Zhou, and G. Zhang, Automatic clustering method based on evolutionary optimization,IET Computer Vision, 7(4): 258–271, 2013.
[6]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.
[5]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.
[4]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.
[3]A.Zhou, Q. Zhang, Y. Jin, B. Sendhoff, and E. Tsang, Modelling the populationdistribution in multi-objective optimization by generative topographic mapping,inParallel Problem Solving From Nature(PPSN IX), LNCS(4193), Reykjavik, Iceland: Springer-Verlag, 2006, pp.443-452.
[2]A. Zhou, H. Cao, L. Kang, and Y. Huang, The automatic modelling of complex functions based on genetic programming,Journalof System Simulation, 15(6):797–799, 2003.
[1]A. Zhou, L. Kang, Y. Chen, and Y. Huang, A new definition and calculation model for evolutionary multi-objective optimization,Journal of Wuhan University, 8(1B):189–194, 2003.
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