Zexuan Zhu

Publications

Journal Papers




1. Z. Liang, Y. Zou, S. Zheng, S. Yang, and Z. Zhu*, A feedback-based prediction strategy for dynamic multi-objective evolutionary optimization, Expert Systems with Applications, 2021. (accepted)
2. 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, 2021. (accepted)
3. W. Lin, Q. Lin, J. Jia, Z. Zhu, C. A. Coello Coello, K.-C. Wong, Decomposition-based multiobjective optimization with bicriteria assisted adaptive operator selection, Swarm and Evolutionary Computation, vol. 60, article no. 100790, 2021.
4. Z. Liang, J. Zeng, L. Liu*, and Z. Zhu*, A many-objective optimization algorithm with mutation strategy based on variable classification and elite individual, Swarm and Evolutionary Computation, vol. 60, article no. 100769, 2021.
5. 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, 2020, (accepted)
6. 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, 2020. (accepted)
7. 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, 2020, (accepted)
8. 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
9. Z. Liang, H. Dong, C. Liu, W. Liang, and Z. Zhu*, Evolutionary multi-tasking for multi-objective optimization with subspace alignment and adaptive differential evolution, IEEE Transactions on Cybernetics, 2020. (accepted, Code)
10. Z. Liang, T. Wu, X. Ma, Z. Zhu*, and S. Yang, A dynamic multi-objective evolutionary algorithm based on decision variable classification, IEEE Transactions on Cybernetics, 2020. (accepted, Code)
11. L. Zhou, L. Feng, K. Tan, J. Zhong, Z. Zhu, K. Liu, and C. Chen, Towards adaptive knowledge transfer in multifactorial evolutionary computation, IEEE Transactions on Cybernetics, 2020. (accepted)
12. N. Wu, F. Yin, L. Ou-Yang, Z. Zhu*, and W. Xie, Joint learning of multiple gene networks from single-cell gene expression data, Computational and Structural Biotechnology Journal, vol. 18, pp. 2583-2595, 2020
13. 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, 2019. (accepted,Code)
14. L. Feng, L. Zhou, A. Gupta, J. Zhong, Z. Zhu, K. Tan, and A.K. Qin, Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking, IEEE Transactions on Cybernetics, 2019. (accepted)
15. W. Chen, Z. Zhu, and Shan He, MUMI: Multitask module identification for biological networks, IEEE Transactions on Evolutionary Computation, vol.‏24, no.4, pp. 765-776, 2020
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, 2019 (in press, Code).
17. N. Shi, Z. Zhu, K. Tang, D. Parker, and S. He, ATEN: And/Or Tree Ensemble for inferring accurate Boolean network topology and dynamics, Bioinformatics, vol. 36, no. 2, pp. 578-585, 2020
18. X. Ma, Q. Chen, Y. Yu, Y. Sun, L. Ma and Z. Zhu*, A two-level transfer learning algorithm for evolutionary multitasking, Frontiers in Neuroscience,vol. 13, article no. 1408, 2020..
19. Z. Liang, J. Zhang, L. Feng, and Z. Zhu*, A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking, Expert Systems with Applications, vol. 138, article no. 117798, 2019 (Code)
20. J. Huang, M. Wu, F. Lu, L. Ou-Yang* and Z. Zhu*,Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization, BMC Bioinformatis, vol. 20, no. (Suppl 19), articla no. 657, 2019.
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. N. Wu, J. Huang, X. F. Zhang, L. Ou-Yang*, S. He, Z. Zhu*, and W. Xie, Weighted fused pathway graphical lasso for joint estimation of multiple gene networks,Frontiers in Genetics, vol. 10, pp. 623, 2019.
23. L. Ou-Yang, J. Huang, X.-F. Zhang, Y.-R. Li, Y. Sun, S. He, and Z. Zhu*, LncRNA-disease association prediction using two-side sparse self-representation, Frontiers in Genetics, vol. 10, pp. 476, 2019.
24. Y.-A. Huang, Z.-A. Huang, Z.-H. You*, Z. Zhu*, C.-Q. Yu, W. Huang, and J. Guo, Predicting lncRNA-miRNA interaction via graph convolution auto-encoder, Frontiers in Genetics, vol. 10, pp. 758, 2019.
25. W. Lin, Q. Z. Lin, Z. Zhu, J. Q. Li, J. Y. Chen, and Z. Ming, Evolutionary search with multiple Utopian reference points in decomposition-based multiobjective optimization, Complexity, article no. 7436712, 2019.
26. X. Chen, Y.-Z. Sun, N.-N. Guan, J. Qu, Z.-A. Huang, Z. Zhu, J.-Q. Li, Computational models for lncRNA function prediction and functional similarity calculation, Briefings in Functional Genomics, vol. 18, no. 1, pp. 58-82, 2019.
27. Z. Liang, W. Hou, X. Huang, and Z. Zhu*, Two new reference vector adaptation strategies for many-objective evolutionary algorithms, Information Sciences, vol. 483, pp.332-349, 2019.(Code)
28. Z. Liang, S. Zheng. Z. Zhu*, and S. Yang, Hybrid of memory and prediction strategies for dynamic multiobjective optimization, Information Sciences, vol. 485, pp.200-218, 2019.(Code)
29. L. Cui, G. Li, Z. Zhu, Z. Ming, Z. Wen, and N. Lu, Differential evolution algorithm with dichotomy-based parameter space compression, Soft Computing, vol. 23, no. 11, pp. 3643-3660, 2019.
30. Z.-A Huang, Y.-A Huang, Z.-H You, Z. Zhu, and Y Sun, Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph, BMC Medical Genomics， vol. 11, no. 6, pp. 113, 2018.
31. Y. Sun, Z. Zhu, Z.-H You, Z. Zeng, Z.-A Huang, and Y.-A Huang, FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases, BMC Systems Biology, vol. 12, no. 9, pp. 121, 2018.
32. Y. Sun, K. Tang, Z. Zhu, and X. Yao, Concept drift adaptation by exploiting historical knowledge, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4822-4832, 2018.
33. Y. Sun, P. Du, X. Lu, P. Xie, Z. Qian, S. Fan*, and Z. Zhu*, Quantitative characterization of bovine serum albumin thin-films using terahertz spectroscopy and machine learning methods, Biomedical Optics Express, vol. 9, no. 7, pp. 2917-2929, 2018.
34. 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)
35. 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)
36. L. Cui, G. Li, Z. Zhu, Q. Lin, K. C. Wong, J. Chen, N. Lu, and J. Lu, Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism, Information Sciences, vol. 422, pp. 122-143, 2018.
37. H.-J. Zhu, Z. H. You*, Z. Zhu*, W.-L. Shi, X. Chen, L. Cheng, DroidDet: Effective and robust detection of Android malware using static analysis along with rotation forest model, Neurocomputing, vol. 272, pp.638-646, 2018.
38. Y. Zhong, A. Ma, Y. Ong, Z. Zhu, and L. Zhang, Computational intelligence in optical remote sensing image processing, Applied Soft Computing, vol 64, pp. 75-93, 2018.
39. L. Cui L, G. Li, Z. Zhu, Z. Wen, N. Lu, and J. Lu, A novel differential evolution algorithm with a self-adaptation parameter control method by differential evolution, Soft Computing, vol. 22, no. 18, pp. 6171-6190, 2018.
40. M. Gong, Z. Wang, Z. Zhu, L. Jiao. A Similarity-Based Multiobjective Evolutionary Algorithm for Deployment Optimization of Near Space Communication System. IEEE Transactions on Evolutionary Computation, vol. 21, no. 6, pp.878-897, 2017.
41. F. Wang, Z.-A. Huang, X. Chen*, Z. Zhu*, Z. Wen, J. Zhao, G.-Y. Yan, LRLSHMDA: Laplacian regularized least squares for human microbe–disease association prediction, Scientific Reports, vol. 7, artical no. 7601, 2017.
42. L. Cui, G. Li, Z. Zhu, Q. Lin, Z. Wen, N. Lu, K.-C. Wong, and J. Chen, A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization, Information Sciences, vol. 414, pp. 53-67, 2017
43. Z. Liang, R. Guo, J. Sun, Z. Ming, and Z. Zhu*, Orderly roulette selection based ant colony algorithm for hierarchical multilabel rrotein function prediction, Mathematical Problems in Engineering, article no. 6320273, 2017
44. Z. Liang, K. Hu, Q. Zhu, and Z. Zhu*,An enhanced artificial bee colony algorithm with adaptive differential operators, Applied Soft Computing, vol. 58, pp. 480-492, 2017.
45. 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)
46. 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)
47. Z.-A. Huang, X. Chen*, Z. Zhu*, H. Liu, G.-Y. Yan, Z.-H. You, and Z. Wen, PBHMDA: Path-based human microbe-disease association prediction, Frontiers in Microbiology, vol. 8, article no. 233, 2017.(Code and Datasets)
48. D. Li, Z. Pan, G. Hu, Z. Zhu, and S. He, Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme, BMC Genomics, vol. 18, no.Suppl 2, article no. 209, 2017.
49. Y. Sun, S. Yang, P. Du, F. Yan, J. Qu, Z. Zhu, J. Zuo, and C. Zhang, Investigate the effects of EG doping PEDOT/PSS on transmission and anti-reflection properties using terahertz pulsed spectroscopy, Optics Express, vol. 25, no. 3, pp. 1723-1731, 2017
50. S. He, G. Jia, Z. Zhu, D. Tennant, Q. Huang, K. Tang, J. Liu, M. Musolesi, J. Heath, and X. Yao, Cooperative co-evolutionary module identification with application to cancer disease module discovery, IEEE Transactions on Evolutionary Computation, vol. 20, no. 6, pp. 838-858, 2016.
51. Y. Zhong, Z. Zhu, Y. Ong, Soft computing in remote sensing image processing, Soft Computing, vol. 20, no. 12, pp.4629-4630, 2016.
52. J. Yang, J. Zhou, Z. Zhu, X. Ma, and Z. Ji, Iterative ensemble feature selection for multiclass classification of imbalanced microarray data, Journal of Biological Research, vol. 23(Suppl 1), article no. 13 2016.
53. Q. Zhu, Q. Lin, Z. Du, Z. Liang, W. Wang, Z. Zhu, J. Chen, P. Huang, and Z. Ming, A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm, Information Sciences, vol. 345, pp. 177-198, 2016
54. Z. Zhu, J. Xiao, S. He, Z. Ji, and Y. Sun, A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem, Information Sciences, vol. 329, pp. 73-89, 2016. (Datasets)
55. J. Yang, J. Zhou, Z. Zhu, and Z. Ji, Ensemble feature selection based on constrained niching particle swarm optimization for omics data classification, Journal of Signal Processing, vol. 32, no. 7, pp. 757-763, 2016.(In Chinease)
56. J Yang, Z. Ji, W. Xie, and Z. Zhu, Model selection based on particle swarm optimization for omics data classification, Journal of Shenzhen University Science and Engineering, vol. 33, no. 3, pp. 264-271, 2016.(In Chinese)
57. Z. Zhu, J. Xiao, J.-Q. Li, F. Wang, and Q. Zhang, Global path planning of wheeled robots using multi-objective memetic algorithms, Integrated Computer-Aided Engineering, vol. 22, no. 4, pp. 387-404, 2015.
58. Z. Zhu, F. Wang, S. He, and Y. Sun, Global path planning of mobile robots using a memetic algorithm, International Journal of Systems Science, vol. 46, no. 11, pp. 1982-1993, 2015.
59. 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)
60. 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.
61. Z. Zhu, S. Jia, S. He, Y. Sun, Z. Ji, L. Shen, Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic framework, Information Sciences, vol. 298, pp. 274–287, 2015.
62. S. He, H. Chen, Z. Zhu*, D. G. Ward, H. J. Cooper, M. R. Viantd, J. K. Heath, and Xin Yao, Robust twin boosting for feature selection from high-dimensional omics data with label noise, Information Sciences, vol. 291, pp. 1-8, 2015.
63. 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)
64. J. Zhou, J. Yang, L. Lin,Z. Zhu, and Z. Ji, A local best particle swarm optimization based on crown jewel defense strategy, International Journal of Swarm Intelligence Research, vol. 6, no. 1, pp. 41-63, 2015
65. J Zhou, Z. Ji, Z. Zhu, S. He, Compression of next-generation sequencing quality scores using memetic algorithm, BMC Bioinformatics, 15(Suppl 15):S10, 2014
66. A. Zhou, R. J. M. Weber, J. W. Allwood, R. Mistrik, Z. Zhu, Z. Ji, S. Chen, W. Dunn, S. He, and M Viant, HAMMER: Automated operation of Mass Frontier to construct in-silico mass spectral fragmentation libraries, Bioinformatics, vol. 30, no. 4, pp. 581-583, 2014.
67. Y. Liu, D. Tennant, Z. Zhu, J. Heath, X. Yao, S. He, DiME: A scalable disease module identification algorithm with application to glioma progression, PLoS ONE, vol. 9, no. 2, e86693, 2014.
68. S. Jia, Z. Zhu, L. Shen, and Q. Li, A two-stage feature selection framework for hyperspectral image classification using few labeled samples, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 3, pp. 1023-1035, 2014.
69. J. Zhou, Z. Zhu, and Z. Ji, A memetic algorithm based feature weighting for metabolomics data classification, Acta Electronica Sinica, vol. 23, no. 4, pp. 706-711, 2014.
70. Z. Zhu, Y. Zhang, Z. You, L. Jiang, and Z. Ji, Advances in the compression of high-throughput DNA sequencing data, Journal of Shenzhen University Science and Engineering, vol. 30, no. 4, pp. 409-416, 2013.(In Chinese)
71. L. Shen, Z. Zhu, S. Jia, J. Zhu, and Y. Sun, Discriminative Gabor feature selection for hyperspectral image classification, IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 1, pp. 29-33, 2013.
72. Z. Ji, J. Zhou, Z. Zhu, Self-configuration single particle optimizer for DNA sequence compression, Soft Computing, vol. 17, no. 4, pp. 675-682, 2013
73. J. Zhou, Z. Ji, Z. Zhu, S.-P. Chen, Intelligent DNA sequence data compression using memetic algorithm, Acta Electronica Sinica, vol. 41, no. 3, pp. 513-518, 2013 (In Chinese)
74. Y. Sun, Z. Zhu, S. Chen, J. Balakrishnan, D. Abbott, A. T. Ahuja, and E. MacPherson, Observing the temperature dependent transition of the GP2 peptide using Terahertz spectroscopy, PLoS ONE, vol. 7, no. 11, e50306, 2012.
75. 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.
76. Z. Ji, J. Zhou,Z. Zhu and Q. H. Wu, Bioinformatics features based DNA sequence data compression algorithm, Acta Electronica Sinica, vol. 39, no. 5, pp. 991-995, 2011.(In Chinese)
77. Z. Ji, T. Tian andZ. Zhu, The survey on evolvable hardware research, Journal of Shenzhen University Science and Engineering, vol. 28, no. 3, pp. 255-263, 2011. (In Chinese)
78. 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.
79. 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)
80. J.-Q. Li, Z. Zhu*, Z. Ji, H.-L. Pei, Invariant sets of hybrid autonomous systems with disturbance, Mathematical Problems In Engineering,  vol. 2010, article no.: 289678, 2010.
81. X. F. Fan,Z. Zhu, L. Liu, Z. X. Shen, and J.-L. Kuo, Theoretical study on structural stability of alloy cages: A case of silicon-doped heterofullerenes, Communications in Computational Physics. vol. 8, pp. 289-303, 2010.
82. X. F. Fan, Z. Zhu, Z. X. Shen, and Jer-Lai Kuo, On the use of bond counting rules in prediciting the stability of C12B6N6 fullerene, Journal of Physical Chemistry C, Vol. 112, No. 40. pp. 15691-15696, 2008.
83. H. J. Rong, Y. S. Ong, A. H. Tan, and Z. Zhu, A fast pruned-extreme learning machine for classification problem, Neurocomputing, vol. 72, no. (1-3), pp. 359-366, 2008.
84. 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)
85. 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.
86. X. F. Fan, Z. Zhu, Y. S. Ong, Y. M. Lu, Z. X. Shen, and Jer-Lai Kuo, A direct first principle study on the structure and electronic properties of BexZn1-xO, Applied Physics Letters, vol. 91, 121121, 2007.(Code)




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Conference Papers & Book Chapter

 

1. Y. Zheng, Z. Zhu, Y. Qi, L. Wang, and X. Ma, Multi-objective multifactorial evolutionary algorithm enhanced with the weighting helper-task. 2020 2nd International Conference on Industrial Artificial Intelligence (IAI 2020), Oct 23-25, Shenyang, China, 2020.
2. Z. Zhou, X. Ma, Z. Liang, and Z. Zhu, Multi-objective multi-factorial memetic algorithm based on bone route and large neighborhood local search for VRPTW, 2020 IEEE Congress on Evolutionary Computation (CEC 2020), July 19-24, Glasgow, UK, 2020.
3. W. Zhou, L. Feng, Z. Zhu, K. Liu, C. Chen, and Z. Wu, Tracking moving optima of dynamic multi-objective problem via prediction in objective space, 2020 IEEE Congress on Evolutionary Computation (CEC 2020), July 19-24, Glasgow, UK, 2020.
4. X. Zhang and Z. Zhu, Sparse multi-task least-squares support vector machine, 2020 International Conference on Neural Computing for Advanced Applications (NCAA 2020), July 3-5, Shenzhen, China, 2020.
5. J. Chen，X. Ma, Y. Sun, and Z. Zhu, A reference point-based evolutionary algorithm for many-objective fuzzy portfolio selection, The 14th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2019), November 22-25, Zhengzhou, China, 2019.
6. R. Shi, W. Lin, Q. Lin, Z. Zhu and J. Chen, Multimodal multi-objective optimization using a density-based one-by-one update strategy, 2019 IEEE Congress on Evolutionary Computation (CEC2019), June 10-13, Wellington, New Zealand, 2019.
7. Z. Zhou, X. Ma and Z. Zhu, Multi-objective memetic algorithm based on correlation priority for pickup-and-delivery problems，2019 IEEE Congress on Evolutionary Computation (CEC2019), June 10-13, Wellington, New Zealand, 2019.
8. Y. Yu, A. Zhu, Z. Zhu, Q. Lin, J. Yin and X. Ma, Multifactorial differential evolution with opposition-based learning for multitasking optimization, 2019 IEEE Congress on Evolutionary Computation (CEC2019), June 10-13, Wellington, New Zealand, 2019.
9. J. Yin, A. Zhu, Z. Zhu, Y. Yu and X. Ma, Multifactorial evolutionary algorithm enhanced with cross-task search direction, 2019 IEEE Congress on Evolutionary Computation (CEC2019), June 10-13, Wellington, New Zealand, 2019.
10. C. Peng, Q. Deng, Z.-A. Huang, Y. Sun and Z. Zhu, G-FQZip: Lossless reference-based compression of FASTQ files using GPUs, The 13th International Conference on Computational Intelligence and Security (CIS 2017), December 15-18, HongKong, 2017.
11. Q. Chen, X. Ma, Z. Zhu, and Y. Sun, Evolutionary multi-tasking single-objective optimization based on cooperative coevolutionary memetic algorithm, The 13th International Conference on Computational Intelligence and Security (CIS 2017), December 15-18, HongKong, 2017
12. Y. Yang, X. Ma, Y. Sun, and Z. Zhu, Three-dimentional dynamic request prediction based multi-objective memetic algorithm for pickup-and-delivery problem with time windows, The 11th International Conference on Simulated Evolution and Learning (SEAL 2017), November 10-13, Shenzhen, China, 2017.
13. Q. Chen, X. Ma, Y. Sun, and Z. Zhu, Adaptive memetic algorithm based evolutionary multi-tasking single-objective optimization,The 11th International Conference on Simulated Evolution and Learning (SEAL 2017), November 10-13, Shenzhen, China, 2017.
14. Y. Yang, Y. Sun and Z. Zhu, Multi-objective memetic algorithm based on request prediction for dynamic pickup-and-delivery problems, 2017 IEEE Congress on Evolutionary Computation (CEC2017), June 5-8, Donostia - San Sebastián, Spain, 2017.
15. L. Feng, W. Zhou, L. Zhou, S.-W. Jiang, J. Zhong, B. Da, Z. Zhu and Y. Wang, An empirical study of multifactorial PSO and multifactorial DE, 2017 IEEE Congress on Evolutionary Computation (CEC2017), June 5-8, Donostia - San Sebastián, Spain, 2017.
16. L. Zhou, L. Feng, J. Zhong, Y. Ong, Z. Zhu, and E. H. Sha, Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem, 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016), December 6-9, Athens, Greece, 2016
17. X. Ma, Z. Zhu, Z. Ji, J. Yang and N. Wu, A comparative study on decomposition-based multi-objective evolutionary algorithms for many-objective optimization, 2016 IEEE Congress on Evolutionary Computation (CEC2016), July 25-29, Vancouver, Canada, 2016.
18. J. Xiao, Y. Yang, X. Ma, J. Zhou, and Z. Zhu, Multi-objective memetic algorithm for solving pickup and delivery problem with dynamic customer requests and traffic information, 2016 IEEE Congress on Evolutionary Computation (CEC2016), July 25-29, Vancouver, Canada, 2016.
19. N. Wu, J. Zhou, Z. Zhu, and Z. Ji, A more efficient method for domain repeat detection in WD-40 proteins, 2016 IEEE Congress on Evolutionary Computation (CEC2016), July 25-29, Vancouver, Canada, 2016.
20. J. Zhou, Z. Ji, Z. Zhu, and S. He, Metabolomics biomarker discovery using multimodal memetic algorithm and multivariate mutual information based feature selection, 2016 IEEE Congress on Evolutionary Computation (CEC2016), July 25-29, Vancouver, Canada, 2016.
21. G. Jia, S. He, Z. Zhu, J. Liu, K. Tang, A multimodal optimization and surprise based consensus community detection algorithm, 2015 Genetic and Evolutionary Computation Conference (GECCO2015), July 11-15, Madrid, Spain, 2015
22. S. Jia, Y. Xie, and Z. Zhu, Integration of spatial and spectral information by means of sparse representation-based classification for hyperspectral imagery, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014), November 10-12, Singapore, 2014.
23. Y. Zhang, L. Li, J. Xiao, Y. Yang and Z. Zhu, FQZip: lossless reference-based compression of next generation sequencing data in FASTQ format, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014), November 10-12, Singapore, 2014.
24. F. Wang, Y. Gao and Z. Zhu, Locality-sensitive hashing based multiobjective memetic algorithm for dynamic pickup and delivery problems, 2014 IEEE Congress on Evolutionary Computation (CEC’2014), July 6-11, Beijing, China, 2014.
25. Y. Chen, Z. Zhuand Z. Ji, Feature extraction based on trimmed complex network representation for metabolomic data classification, 2014 IEEE Congress on Evolutionary Computation (CEC’2014), July 6-11, Beijing, China, 2014.
26. N. Wu, Z. Zhu and Z. Ji, A growing partitional clustering based on particle swarm optimization, 2014 IEEE Congress on Evolutionary Computation (CEC’2014), July 6-11, Beijing, China, 2014.
27. Q.-Y Huang, Z. -H You, S. Li and Z. Zhu, Using Chou's amphiphilic pseudo-amino acid composition and extreme learning machine for prediction of protein-protein interactions, 2014 IEEE Congress on Evolutionary Computation (CEC’2014), July 6-11, Beijing, China, 2014.
28. F. Wang and Z. Zhu, Global path planning of wheeled robots using a multi-objective memetic algorithm, The 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'2013), Octobor 20-13, Hefei, China, 2013.
29. Z. You, Z. Ming, B. Niu, S. Deng, and Z. Zhu, A SVM-based system for predicting protein-protein interactions using a novel representation of protein sequences, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 7995 LNCS, p 629-637, 2013, the 9th International Conference on Intelligent Computing, ICIC 2013, July 28-31, Nanjing, China, 2013.
30. Y. Sun, Z. Zhu, S. He and Z. Ji, A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting, IEEE Symposium Series on Computational Intelligence (SSCI 2013), April 15-19, Singapore,2013.
31. J. Yang, Z. Zhu, S. He, and Z. Ji, Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification, IEEE Symposium Series on Computational Intelligence (SSCI 2013), April 15-19, Singapore,2013.
32. Z. Zhu, W. Liu, S. He and Z. Ji，Memetic clustering based on particle swarm optimizer and k-means，2012 IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, June 10-15, 2012
33. Z. Ji, T. Tian, S. He and Z. Zhu, A memory binary particle swarm optimization, 2012 IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, June 10-15, 2012.
34. Lan Tao, Ning Fan and Z. Zhu, A hybrid evolutionary algorithm for promoter recognition, the 5th International Conference on BioMedical Engineering and Informatics (BMEI 2012), 932-935, 2012.
35. W. Liu, Z. Ji and Z. Zhu, Survival analysis of gene expression data using PSO based radial basis function networks, 2012 IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, June 10-15, 2012.
36. L. Lin, Z. Ji and Z. Zhu, A crown jewel defense strategy based particle swarm optimization, 2012 IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, June 10-15, 2012.
37. Z. Zhu, L. Shen, Y. Sun, S. He, and Z. Ji, Memetic three-dimensional Gabor feature extraction for hyperspectral imagery classification, The 3rd International Conference on Swarm Intelligence (ICSI 2012), June 17-20, 2012, Shenzhen, China.
38. J. Zhou, Z. Ji, and Z. Zhu, A coevolutionary memetic particle swarm optimizer, The 3rd International Conference on Swarm Intelligence (ICSI 2012), June 17-20, 2012, Shenzhen, China.
39. Z. You, Y. Lei, Z. Ji, and Z. Zhu, A novel approach to modeling protein-protein interaction networks, The 3rd International Conference on Swarm Intelligence (ICSI 2012), June 17-20, 2012, Shenzhen, China.
40. Z. Zhu, Z. Ji, X. F. Xiao and J.-L. Kuo, Memetic figure selection for cluster expansion in binary alloy systems, 2011 IEEE Symposium Series on Computational Intelligence (SSCI 2011) - - 2011 IEEE Workshop on Memetic Computing (MC 2011), pp. 15-20, 2011.
41. J. Zhou, Z. Ji, L. Shen, Z. Zhu and S. Chen, PSO based memetic algorithm for face recognition Gabor filters selection, 2011 IEEE Symposium Series on Computational Intelligence (SSCI 2011) - - 2011 IEEE Workshop on Memetic Computing (MC 2011), pp. 9-14, 2011.
42. Y. Sun, E. MacPherson, Z. Zhu, Dielectric properties of monoclonal antibodies to influenza using Terahertz spectroscopy,2011 International Conference on Opto-Electronics Engineering and Information Science (ICOEIS 2011), 2011.
43. Z. Ji, W. Liu and Z. Zhu, Gene clustering using particle swarm optimizer based memetic algorithm, The Second International Conference on Swarm Intelligence (ICSI 2011), Lecture Notes in Computer Science, vol. 6728, PART 1, pp. 587-594, 2011.
44. L. Tao, H. Chen, Y. Xu and Z. Zhu, A new promoter recognition method based on features optimal selection, The 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2011), pp. 1-4, 2011.
45. Z. Zhu, Z. Ji, and S. Jia, Memetic ant colony optimization for band selection of hyperspectral imagery classification, CCPR 2010, pp. 1012-1017, October 21-23, Chongqing, China, 2010.
46. S. Jia, Z. Ji, Z. Zhu, and Y. Qian, Feature selection technique for hyperspectral imagery classification with noise reduction preprocessing. CCPR 2010, pp. 325-329, October 21-23, Chongqing, China, 2010.
47. Z. Zhu, S. Jia, and Z. Ji, Affinity propagation based memetic band selection on hyperspectral imagery datasets, IEEE CEC 2010 July 18-23, Barcelona, Spain, 2010.
48. Z. Zhu, Yew-Soon Ong, and Jer-Lai Kuo, Feature selection using single/multi-objective memetic algorithms,Chapter 6, pp. 111-132, Multi-objective Memetic Algorithms, Springer Series of Studies in Computational Intelligence, vol. 171, Goh, Chi-Keong; Ong, Yew-Soon; Tan, Kay Chen. ISBN: 978-3-540-88050-9, 2009
49. Z. Zhu, X. F. Fan, Y. S. Ong, and Jer-Lai Kuo, Prediction of binary alloy properties using computational intelligence techniques, The 3rd MRS-S Conference on Advanced Materials, Institute of Materials Research and Engineering, Singapore, 25 - 27 Feb, 2008.
50. Jer-Lai Kuo, X. F. Fan, Z. Zhu, Y. S. Ong, A grid-ready multi-scale method to study order/disorder transitions in ice physics and semiconductor alloys, The 3rd MRS-S Conference on Advanced Materials, Institute of Materials Research and Engineering, Singapore, 25 - 27 Feb, 2008.
51. H. Wu, X. F. Fan, Z. Zhu, and Jer-Lai Kuo, Inequivalent cell shapes for HCP lattice and the corresponding configurations of binary alloys, The 3rd MRS-S Conference on Advanced Materials, Institute of Materials Research and Engineering, Singapore, 25 - 27 Feb, 2008.
52. Z. Zhu and Y. S. Ong, Memetic algorithms for feature selection on microarray data, Proceedings of the 4th International Symposium on Neural Networks: Advances in Neural Networks (ISNN2007), pp.1327-1335, Nanjing, China, 3-7 Jun, 2007.
53. Z. Zhu, Y. S. Ong, K. W. Wong and K. T. Seow, Experimental condition selection in whole-genome functional classification, Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS2004), pp. 295-300, Singapore, December 2004.
54. S. K. Ng, Z. Zhu and Y. S. Ong,  Whole-genome functional classification of genes by latent semantic analysis on microarray data, Proceedings of the second conference on Asia-Pacific bioinformatics (APBC2004), pp. 123-129, Dunedin, New Zealand, 18 - 22 Jan 2004.