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周爱民

职称: 研究员

直属机构: 金沙集团官方网站

学科:

10 访问

相关教师

个人资料

  • 部门: 金沙集团官方网站
  • 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

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:19511120600,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: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:

[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, in AAAIpp. 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, in Parallel 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 Computation42:92-108, 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 Letters6(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,in Parallel 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