Zexuan Zhu 朱泽轩

About me

 

Zexuan ZHU is a Professor with the College of Computer Science and Software Engineering, Shenzhen University, China. He received his B.Sc degree from the Department of Computer Science and Engineering, Fudan University, China, in 2003 and the Ph.D degree from the School of Computer Science and Engineering, Nanyang Technological University, Singapore, in 2008. His research interests include computational intelligence, machine learning, and bioinformatics. He is an Associate Editor of IEEE Transactions on Evolutionary Computation and IEEE Transactions on Emerging Topics in Computational Intelligence, and serves as the Editorial Board Member of Memetic Computing Journal. He is also the Chair of the IEEE CIS Emergent Technologies Task Force on Memetic Computing.

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Selected publications (Full publication list，Google Scholar)

 

  1. T. Dai, M. Ya, J. Li, X. Zhang, S.-T. Xia, and Z. Zhu*, CFGN: A lightweight context feature guided network for image super-resolution, IEEE Transactions on Emerging Topics in Computational Intelligence, 2023 (accepted)
  2. Z. Liu, G. Li, H. Zhang, Z. Liang, and Z. Zhu*, Multifactorial evolutionary algorithm based on diffusion gradient descent, IEEE Transactions on Cybernetics, 2023 (accepted)
  3. L. Liu, W. Yuan, Z. Liang, X. Ma, and Z. Zhu*, Construction of polar codes based on memetic algorithm, IEEE Transactions on Emerging Topics in Computational Intelligence, 2022 (accepted)
  4. Z. Liang, Y. Zhu, X. Wang, Z. Li, and Z. Zhu*，Evolutionary multitasking for multi-objective optimization based on generative strategies, IEEE Transactions on Evolutionary Computation, 2022 (accepted)
  5. X. Ma, Z. Huang, X. Li, Y. Qi, L. Wang, and Z. Zhu*, Multiobjectivization of single-objective optimization in evolutionary computation: A survey, IEEE Transactions on Cybernetics, vol. 53, no. 6, pp. 3702-2715, 2023.
  6. X. Ma, Z. Huang, X. Li, L. Wang, Y. Qi, and Z. Zhu*, Merged differential grouping for large-scale global optimization, IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1439-1451, 2022.
  7. M. Yang, Z.-A Huang, W. Gu, K. Han, W. Pan, X. Yang*, and Z. Zhu*, Prediction of biomarker-disease associations based on graph attention network and text representation, Briefings in Bioinformatics, vol. 23, no. 5, pp. 1-14, 2022
  8. S. Xie, T. He, S. He, and Z. Zhu*, CURC: A CUDA-based reference-free read compressor, Bioinformatics, vol. 38, no. 12, pp. 3294-3296, 2022.
  9. Z. Liang, W. Liang, Z. Wang, X. Ma, L. Liu*, and Z. Zhu*, Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution, IEEE Transactions on Systems, Man, and Cybernetics - Systems, vol. 52, no. 7, pp. 4457-4469, 2022.
  10. X. Ma, J. Yin, A. Zhu, X. Li, Y. Yu, L. Wang, Y. Qi, and Z. Zhu*, Enhanced multifactorial evolutionary algorithm with meme helper-tasks, IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7837-7851, 2022.
  11. Z. Liang, H. Dong, C. Liu, W. Liang, and Z. Zhu*, Evolutionary multitasking for multiobjective optimization with subspace alignment and adaptive differential evolution, IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2096-2109, 2022. (Code)
  12. Z. Liang, T. Wu, X. Ma, Z. Zhu*, and S. Yang, A dynamic multiobjective evolutionary algorithm based on decision variable classification, IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1602-1615, 2022. (Code)
  13. Z. Liang, X. Xu, L. Liu*, Y. Tu, and Z. Zhu*，Evolutionary many-task optimization based on multisource knowledge transfer, IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 319-333, 2022.
  14. X. Ma, Y. Zheng, X. Li, L. Wang, Y. Qi, J. Yang and Z. Zhu*, Improving evolutionary multitasking optimization by leveraging inter-task gene similarity and mirror transformation, IEEE Computational Intelligence Magazine, vol. 16, no. 4, pp.38-51, 2021.
  15. Z. Liang, T. Luo, K. Hu, X. Ma, and Z. Zhu*, An indicator-based many-objective evolutionary algorithm with boundary protection, IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4553-2566, 2021. (Code)
  16. Z. Liang, K. Hu, X. Ma, and Z. Zhu*, A many-objective evolutionary algorithm based on a two-round selection strategy, IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1417-1429, 2021 (Code).
  17. Z.-A. Huang, J. Zhang, Z. Zhu*, E. Q. Wu, and K. C. Tan*, Identification of autistic risk candidate genes and toxic chemicals via multi-label learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 3971-3984, 2021.
  18. Z.-A. Huang, Z. Zhu*, C. Yau, and K. C. Tan*, Identifying autism spectrum disorder from resting-state fMRI using deep belief network, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2847-2861, 2021.
  19. Q. Lin, W. Lin, Z. Zhu*, M. Gong, J. Li, and C. A. Coello Coello, Multimodal multi-objective evolutionary optimization with dual clustering in decision and objective spaces, IEEE Transactions on Evolutionary Computation, vol. 25, no. 1, pp. 130-144, 2021.
  20. X. Ma, Y. Yu, X. Li, Y. Qi, and Z. Zhu*, A survey of weight vector adjustment methods for decomposition based multi-objective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.‏24, no.4, pp. 634-649, 2020
  21. X. Ma, X. Li, Q. Zhang, K. Tang, Z. Liang, W. Xie, and Z. Zhu*, A survey on cooperative co-evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol. 23, no. 3, pp. 421-441, 2019.
  22. R. Guo, Y.-R. Li, S. He, L. Ou-Yang, Y. Sun*, and Z. Zhu*, RepLong - de novo repeat identification using long read sequencing data, Bioinformatics, vol. 34, no. 7, pp. 1099-1107, 2018. (Code)
  23. X. Ma, Q. Zhang, G. Tian, J. Yang, and Z. Zhu*, On Tchebycheff decomposition approaches for multi-objective evolutionary optimization, IEEE Transactions on Evolutionary Computation, vol. 22, no. 2, pp. 226-244, 2018. (Code)
  24. Z.-H, You, Z.-A. Huang, Z. Zhu*, G.-Y. Yan, Z.-W. Li, Z. Wen, and X. Chen*, PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction, PLoS Computational Biology, vol. 13, no. 3, artical no. e1005455, 2017.(Code and Datasets)
  25. Z.-A. Huang, Z. Wen, Q. Deng, Y. Chu, Y. Sun, and Z. Zhu*,LW-FQZip 2: a parallelized reference-based compression of FASTQ files, BMC Bioinformatics, vol. 18, no. 1, pp. 179:1-179:8, 2017.(Code)
  26. Z. Zhu, L. Li, Y. Zhang, Y. Yang, and X. Yang, CompMap: a reference-based compression program to speed up read mapping to related reference sequences, Bioinformatics, vol. 31, no. 3, pp. 426-428, 2015.(Code)
  27. Z. Zhu, Y. Zhang, Z. Ji, S. He, and X. Yang, High-throughput DNA sequence data compression, Briefings in Bioinformatics, vol. 16, no. 1, pp. 1-15, 2015.
  28. Y. Zhang, L. Li, Y. Yang, X. Yang, S. He and Z. Zhu*, Light-weight reference-based compression of FASTQ data, BMC Bioinformatics, vol. 16, pp.188, 2015.(Code)
  29. Z. Zhu, J. Zhou, Z. Ji, and Y.-H. Shi, DNA sequence compression using adaptive particle swarm optimization-based memetic algorithm, IEEE Transactions on Evolutionary Computation, vol. 15, no. 5, pp. 643-558, 2011.
  30. Z. Zhu, S. Jia, and Z. Ji, Towards a memetic feature selection paradigm, IEEE Computational Intelligence Magazine, vol. 5, no. 2, pp. 41-53, 2010.
  31. Z. Zhu, Y. S. Ong and M. Zurada, Identification of full and partial class relevant genes, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 2, pp. 263-277, 2010.(Datasets)
  32. Z. Zhu, Y. S. Ong and M. Dash, Markov blanket-embedded genetic algorithm for gene selection, Pattern Recognition, vol. 49, no. 11, pp. 3236-3248, 2007.(Datasets, Code)
  33. Z. Zhu, Y. S. Ong and M. Dash, Wrapper-filter feature selection algorithm using a memetic framework, IEEE Transactions On Systems, Man and Cybernetics - Part B:Cybernetics,  vol. 37, no. 1, pp. 70-76, 2007.(Code)