Aimin Zhou

2004.10-2009.06：Ph.D. in Computer Science, University of Essex, UK

2003.09-2004.09：Ph.D study in Computer Science, Wuhan University, China

2001.09-2003.06：Master in Computer Science, Wuhan University, China

1997.09-2001.06：Bachelor in Computer Science, Wuhan University, China

2016.12-：East China Normal University, Professor

2012.12-2016.12：East China Normal University, Associate Professor

2009.06-2012.12：East China Normal University, Lecturer

Associate Editor,Swarm and Evolutionary Computation

Editorial Board Member, Complex & Intelligent Systems

Editorial Board Member, Chinese Journal of Electronics

Evolutionary Search and Optimization

Explainable Machine Learning

AI for Education 

AI for Science

Introduction to Computer Science, 2022-2023

Artificial Intelligence for Education, 2022-2023
Road of Artificial Intelligence, 2022-2023

Artificial Intelligence, 2010-2023

Selected Topics in Artificial Intelligence, 2021

AIoT System, 2021

Frontier of Artificial Intelligence, 2018-2020

Computational Intelligence, 2012-2016

Optimizaiton Method, 2016-2017

Windows Programming, 2012

Practial Programming, 2010-2012

[7] Data Driven and Knowledge Guided Interpretable Modeling Technology, Science and Technology Commission of Shanghai Municipality, 2019-2022.

[6] Big Data Oriented Fast Magnetic Resonance Imaging, Natural Science Foundation of China (NSFC), 2018-2022.

[5] Model Assisted Evolutionary Multiobjective Optimization and Its Applications, Natural Science Foundation of China (NSFC), 2017-2020.

[4] Recombination Operators based on Learning Techniques for Evolutionary Multiobjective Optimization, Natural Science Foundation of China (NSFC), 2013-2016.

[3] Research and Development of Portable Raman Spectrometer, China National Instrumentation Program, 2013-2016

[2] Information Extraction and Fast Change Detection from Multi-source Heterogeneous Dataset, National Basic Research Program of China (973 Program), 2011-2015.

[1] Estimation of Distribution Algorithms for Multiobjective Traveling Salesman Problems, Natural Science Foundation of China (NSFC), 2011.

Google Citation: http://scholar.google.com/citations?user=E4GQv5cAAAAJ&hl=en

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

Selected Papers:

[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] 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)

[7] 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)

[8] H. Qian, X. Shu, T. Sun, X. Qiu, and A. Zhou, Green derivative-free optimization with dynamic batch evaluation, Journal of Software, 2023. (Accept)

[9] Y. Wu, H. Qian, W. Wang, Y. Zhang, and A. Zhou, A framework for evolutionary large-scale multi-objective security games based on priority priors, Journal of Computer Research and Development, 2023. (Accept)

[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] T. Jin, L. Dou, C. Xiao, W. Zhang, and A. Zhou, Personalized OJ exercise recommendation method with memory and cognition merging, Chinese Journal of Computers, 46(1):103-124, 2023. (in Chinese)

[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] 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. (in Chinese)

[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] X. Chen, C. Shi, A. Zhou, and B. Wu, A multiobjective evolutionary algorithm based on hybrid individual selection mechanism, Journal of Software, 30(12):3651-3664, 2019. (in Chinese)

[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] 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)

[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.

Thesis:

[1] PhD Thesis: Estimation of distribution algorithms for continuous multiobjective optimization, University of Essex, 2009, Supervisors: Prof. Qingfu Zhang, Prof. Edward Tsang, Prof. Yaochu Jin (Honda Research Institute Europe), and Dr. Bernhard Sendhoff (Honda Research Institute Europe).

[2] Master Thesis: Evolutioanry Modeling and Its Applications, Wuhan University, 2003, Supverisor: Prof. Lishan Kang.