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PUBLICATIONS

(Google Scholar Citations)

(Scoups Citations)

 

Edited Books

  1. M. G. Gong, A. M. Zhou, A. K. Qin and L. Q. Pan, ¡°Special issue on collaborative learning and optimization based on swarm and evolutionary computation¡±, Swarm and Evolutionary Computation, vol. 47, 2019¡£

  2. Y. Wu, K. Qin, M. G. Gong and Q. G. Miao, ¡°Special issue on applications of computational intelligence¡±, Electronics, MDPI, 2023.

Referred Journal Articles

  1. M. G. Gong, Y. Q. Zhang, Y. Gao, A. K. Qin, Y. Wu, S. F. Wang and Y. H. Zhang, ¡°A multi-modal vertical federated learning framework based on homomorphic encryption¡±, IEEE Transactions on Information Forensics and Security, DOI: 10.1109/TIFS.2023.3340994, in press, 2023. (SCI IF: 6.8; Q1)

  2. S. Shafiei, H. Dia, W. Wu, H. Grzybowska and A. K. Qin, ¡°An agent-based simulation approach for urban road pricing considering the integration of autonomous vehicles with public transport¡±, IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2023.3315338, in press, 2023. (SCI IF: 8.5; Q1)

  3. M. G. Gong; Y. Gao; Y. Wu; Y. Q. Zhang, A. K. Qin and Y.-S. Ong, ¡°Heterogeneous multi-party learning with data-driven network sampling¡±, IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2023.3290213, in press, 2023. (SCI IF: 23.6; Q1)

  4. Z. H. Cui, T. H. Zhao, L. J. Wu, A. K. Qin and J. W. Li, ¡°Multi-objective cloud task scheduling optimization based on evolutionary multi-factor algorithm¡±, IEEE Transactions on Cloud Computing, DOI: 10.1109/TCC.2023.3315014, in press, 2023. (SCI IF: 6.5; Q1)

  5. Y. Gao, M. G. Gong, Y. Xie, A. K. Qin, K. Pan and Y.-S. Ong, ¡°Multiparty dual learning¡±, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2021.3139076, in press, 2023. (SCI IF: 11.8; Q1)

  6. K. D. Lyu, H. Li, M. Gong, L. N. Xing and A. K. Qin, ¡°Surrogate-assisted evolutionary multiobjective neural architecture search based on transfer stacking and knowledge distillation¡±, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3319567, in press, 2023. (SCI IF:14.3; Q1)

  7. P. Abeysekara, H. Dong and A. K. Qin, ¡°Edge intelligence for real-time IoT service trust prediction¡±, IEEE Transactions on Services Computing, 16(4): 2606-2619, 2023. (SCI IF: 8.1; Q1)

  8. M. Gong, Y. Zhao, H. Li, A. K. Qin, L. N. Xing, J. Z. Li, Y. T. Liu and Y. H. Liu, ¡°Deep fuzzy variable C-means clustering incorporated with curriculum learning¡±, IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2023.3283046, in press, 2023. (SCI IF:11.9; Q1)

  9. Y. Q. Zhang, M. G. Gong, Y. Gao, A. K. Qin, K. Wang, Y. M. Lin and S. F. Wang, ¡°Device-performance-driven heterogeneous multiparty learning for arbitrary images¡±, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2023.3282242, in press, 2023. (SCI IF:10.4; Q1)

  10. P. Abeysekara, H. Dong and A. K. Qin, ¡°Data-driven trust prediction in mobile edge computing-based IoT systems¡±, IEEE Transactions on Services Computing, 16(1): 246-260, 2023. (SCI IF:8.1; Q1)

  11. Y. Gao, M. G. Gong, Y.-S. Ong, A. K. Qin, Y. Wu and F. Xie, ¡°A collaborative multimodal learning-based framework for COVID-19 diagnosis¡±, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2023.3290188, in press, 2023. (SCI IF:10.4; Q1)

  12. J. Shi, T. C. Wu, A. K. Qin, T. Shao, Y. Lei, G. Jeon, ¡°Deep-growing neural network with manifold constraints for hyperspectral image classification¡±, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2023.3292537, in press, 2023. (SCI IF:10.4; Q1)

  13. Y. Wu, Y. Zhang, W. P. Ma, M. G. Gong, X. L. Fan, M. Y. Zhang, A. K. Qin and Q. G. Miao, ¡°RORNet: Partial-to-partial registration network with reliable overlapping representations¡±, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2023.3286943, in press, 2023. (SCI IF:10.4; Q1)

  14. M. G. Gong, W. Y. Qiao, H. Li, A. K. Qin, T. Q. Gao, T. S. Luo and L. N. Xing, ¡°RAFNet: Interdomain representation alignment and fine-tuning for image series classification¡±, IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2023.3302430, in press, 2023. (SCI IF: 8.2; Q1)

  15. D. Zhang, A. K. Qin, Y. Chen and G. X. Lu, ¡°A machine learning approach to predicting mechanical behaviour of non-rigid foldable square-twist origami¡±, Engineering Structures, 278: 115497, 2023. (SCI IF: 5.5; Q1)

  16. Y. Wu, J. M. Liu, M. G. Gong, P. R. Gong, X. L. Fan, A. K. Qin, Q. G. Miao and W. P. Ma ¡°Self-supervised intra-modal and cross-modal contrastive learning for point cloud understanding¡±, IEEE Transactions on Multimedia, DOI: 10.1109/TMM.2023.3284591, in press, 2023. (SCI IF: 7.3; Q1)

  17. H. Li, F. G. Wan, M. G. Gong, A. K. Qin, Y. Wu and L. N. Xing, ¡°Privacy-enhanced multitasking particle swarm optimization based on homomorphic encryption¡±, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3319566, in press, 2023. (SCI IF:14.3; Q1)

  18. Y. T. Liu, H. Li, M. G. Gong and A. K. Qin, ¡°Nonzero degree-based multiobjective cooperative coevolutionary for block sparse recovery¡±, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3264875, in press, 2023. (SCI IF:14.3; Q1)

  19. Y. Xie, Y. F. Liang, M. G. Gong, A. K. Qin, Y.-S. Ong and T. T. He, ¡°Semisupervised graph neural networks for graph classification¡±, IEEE Transactions on Cybernetics, 53(10): 6222-6235, 2023. (SCI IF: 11.8; Q1)

  20. D. Zhang, A. K. Qin, S. Shen, A. Trinchi and G.X. Lu, ¡°Energy absorption analysis of origami structures based on small number of samples using conditional GAN¡±, Thin-Walled Structures, 188: 110772, 2023 (SCI IF: 6.4; Q1)

  21. M. G. Gong, H. Zhou, A. K. Qin, W. F. Liu and Z. Y. Zhao, ¡°Self-paced co-training of graph neural networks for semi-supervised node classification¡±, IEEE Transactions on Neural Networks and Learning Systems, 34(11): 9234-9247, 2023. (SCI IF:10.4; Q1)

  22. J. Shi, T. C. Wu, H. W. Yu, A. K. Qin and G. Jeon, ¡°Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images¡±, Science China Information Sciences, 66(4): 140308, 2023. (SCI IF: 8.8; Q1)

  23. L. Yan, W. L. Qi, A. K. Qin, S. X. Yang, D. W. Gong, B. Y. Qu and J. Liang, ¡°Manifold clustering-based prediction for dynamic multiobjective optimization¡±, Swarm and Evolutionary Computation, 77: 101254, 2023. (SCI IF: 10.0; Q1)

  24. Y. Wu, H. Q. Ding, M. G. Gong, A. K. Qin, W. P. Ma, Q. G. Miao and K. C. Tan, ¡°Evolutionary multiform optimization with two-stage bidirectional knowledge transfer strategy for point cloud registration¡±, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2022.3215743, in press, 2022. (SCI IF:14.3; Q1)

  25. G. Y. Gao, Z. M. Liu, G. J. Zhang, J. Y. Li and A. K. Qin, ¡°DANet: Semi-supervised differentiated auxiliaries guided network for video action recognition¡±, Neural Networks, 158: 121-131, 2023. (SCI IF: 7.8; Q1)

  26. J. Shi, T. C. Wu, A. K. Qin, Y. Lei and G. Jeon, ¡°Semisupervised adaptive ladder network for remote sensing image change detection¡±, IEEE Transactions on Geoscience and Remote Sensing, 60: 5408220, 2022. (SCI IF: 8.2; Q1)

  27. M. G. Gong, F. L. Jiang, A. K. Qin, T. F. Liu, T. Zhan, D. Lu, H. H. Zheng and M. Y. Zhang, ¡°A spectral and spatial attention network for change detection in hyperspectral images¡±, IEEE Transactions on Geoscience and Remote Sensing, 60: 5521614, 2022. (SCI IF: 8.2; Q1)

  28. K.-Y. Feng, X. Fei, M. G. Gong, A. K. Qin, H. Li and Y. Wu, ¡°An automatically layer-wise searching strategy for channel pruning based on task-driven sparsity optimization¡±, IEEE Transactions on Circuits and Systems for Video Technology, 32(9): 5790-5802, 2022. (SCI IF: 8.4; Q1)

  29. N. Sultana, J. Chan, T. Sarwar and A. K. Qin, ¡°Learning to optimise general TSP instances¡±, International Journal of Machine Learning and Cybernetics, 13(8): 2213-2228, 2022. (SCI IF: 5.6; Q1)

  30. J. L. Liu, M. G. Gong, Z. D. Tang, A. K. Qin, H. Li and F. L. Jiang, ¡°Deep image inpainting with enhanced normalization and contextual attention¡±, IEEE Transactions on Circuits and Systems for Video Technology, 32(10): 6599-6614, 2022. (SCI IF: 8.4; Q1)

  31. Z. D. Tang, M. G. Gong, Y. Xie, H. Li and A. K. Qin, ¡°Multi-task particle swarm optimization with dynamic neighbor and level-based inter-task learning¡±, IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2): 300-314, 2022. (SCI IF: 5.3; Q1)

  32. C. J. Zhang, A. K. Qin, W. M. Shen, L. Gao, K. C. Tan and X. Y. Li, ¡°ϵ-Constrained differential evolution using an adaptive ϵ-Level control method¡±, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2): 769-785, 2022. (SCI IF: 8.7; Q1)

  33. Z. D. Tang, M. G. Gong, Y. Wu, A. K. Qin and K. C. Tan, ¡°A multifactorial optimization framework based on adaptive inter-task coordinate system¡±, IEEE Transactions on Cybernetics, 52(7): 6745-6758, 2022. (SCI IF: 11.8; Q1)

  34. S. J. Zhang, M. G. Guo, Y. Xie, A. K. Qin, H. Li, Y. Gao and Y.-S. Ong, ¡°Influence-aware attention networks for anomaly detection in surveillance videos¡±, IEEE Transactions on Circuits and Systems for Video Technology, 32(8): 5427-5437, 2022. (SCI IF: 8.4; Q1)

  35. G. L. Gao, Z. F. Bao, J. Cao, A. K. Qin and T. Sellis, ¡°Location-centered house price prediction: A multi-task learning approach¡±, ACM Transactions on Intelligent Systems and Technology, 13(2): Article No. 32, 2022. (SCI IF: 5.0; Q1)

  36. C. Jacobs, K. Glazebrook, A. K. Qin and T. Collett, ¡°Exploring the interpretability of deep neural networks used for gravitational lens finding with a sensitivity probe¡±, Astronomy and Computing, 38: 100535, 2022. (SCI IF: 2.5; Q2)

  37.  D. A. Tedjopurnomo, Z. F. Bao, B. H. Zheng, F. Choudhury and A. K. Qin, ¡°A survey on modern deep neural network for traffic prediction: trends, methods and challenges¡±, IEEE Transactions on Knowledge and Data Engineering, 34(4): 1544-1561, 2022. (SCI IF: 8.9; Q1)

  38. Z. D. Tang, M. G. Gong, Y. Xie, H. Li and A. K. Qin, ¡°Multi-task particle swarm optimization with dynamic neighbor and level-based inter-task learning¡±, IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2): 300-314, 2022. (SCI IF: 5.3; Q1)

  39. H. Xu, A. K. Qin and S. Y. Xia, ¡°Evolutionary multi-task optimization with adaptive knowledge transfer¡±, IEEE Transactions on Evolutionary Computation, 26(2): 290-303, 2022. (SCI IF:14.3; Q1)

  40. Y. X. Huang, L. Feng, A. K. Qin, M. Chen and K. C. Tan, ¡°Towards large-scale evolutionary multi-tasking: A GPU-based paradigm¡±, IEEE Transactions on Evolutionary Computation, 26(3): 585-598, 2022. (SCI IF:14.3; Q1)

  41. M. G. Gong, S. F. Ji, Y. Xie, Y. Gao and A. K. Qin, ¡°Exploring temporal information for dynamic network embedding¡±, IEEE Transactions on Knowledge and Data Engineering, 34(8): 3754-3764, 2022. (SCI IF: 8.9; Q1)

  42. Y. Wu, J. H. Li, Y. Z. Yuan, A. K. Qin, Q. G. Miao and M. G. Gong, ¡°Commonality autoencoder: Learning common features for change detection from heterogeneous images¡±, IEEE Transactions on Neural Networks and Learning Systems, 33(9): 4257-4270, 2022. (SCI IF:10.4; Q1)

  43. X. L. Fan, M. G. Gong, Y. Wu, A. K. Qin and Y. Xie, ¡°Propagation enhanced neural message passing for graph representation learning¡±, IEEE Transactions on Knowledge and Data Engineering, 35: 1952-1964, 2023. (SCI IF: 8.9; Q1)

  44. H. M. L. Frazer, A. K. Qin, H. Pan and P. Brotchie, ¡°Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset¡±, Journal of Medical Imaging and Radiation Oncology, 65:529-537, 2021. (SCI IF: 1.6; Q3)

  45. W. F. Liu, M. G. Gong, Z. D. Tang, A. K. Qin, K. Sheng and M. L. Xu ¡°Locality preserving dense graph convolutional networks with graph context-aware node representations¡±, Neural Networks, 143: 108-120, 2021. (SCI IF: 7.8; Q1)

  46. Y. Xie, C. Chen, M. G. Gong, D. Y. Li and A. K. Qin, ¡°Graph embedding via multi-scale graph representations¡±, Information Sciences, 578: 102-115, 2021. (SCI IF: 8.1; Q1)

  47. D. A. Tedjopurnomo, X. C. Li, Z. F. Bao, G. Cong, F. Choudhury and A. K. Qin, ¡°Similar trajectory search with spatio-temporal deep representation learning¡±, ACM Transactions on Intelligent Systems and Technology, 12(6): Article No. 77, 2021. (SCI IF: 5.0; Q1)

  48. X. Zhou, A. K. Qin, M. G. Gong and K. C. Tan, ¡°A survey on evolutionary construction of deep neural networks¡±, IEEE Transactions on Evolutionary Computation, 25(5): 894-912, 2021. (SCI IF: 14.3; Q1)

  49. M. G. Gong, J. Liu, A. K. Qin, K. Zhao and K. C. Tan, ¡°Evolving deep neural networks via cooperative coevolution with backpropagation¡±, IEEE Transactions on Neural Networks and Learning Systems, 32(1): 420-434, 2021. (SCI IF: 10.4; Q1)

  50. M. G. Gong, Y. Gao, Y. Xie and A. K. Qin, ¡°An attention-based unsupervised adversarial model for movie review spam detection¡±, IEEE Transactions on Multimedia, 23: 784-796, 2021. (SCI IF: 7.3; Q1)

  51. L. Feng, L. Zhou, A. Gupta, J. H. Zhong, Z. X. Zhu, K. C. Tan and K. Qin, ¡°Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking¡±, IEEE Transactions on Cybernetics, 51(6): 3171-3184, 2021. (SCI IF: 11.8; Q1)

  52. Z. M. Liu, J. Y. Li, G. Y. Gao and A. K. Qin, ¡°Temporal memory network towards real-time video understanding¡±, IEEE Access, 8: 223837-223847, 2020. (SCI IF: 3.9; Q1)

  53. M. G. Gong, K. Pan, Y. Xie, A. K. Qin and Z. D. Tang, ¡°Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition in neural networks¡±, Neural Networks, 125: 131-141, 2020. (SCI IF: 7.8; Q1)

  54. Y. Xie, C. Y. Yao, M. G. Gong, C. Chen and A. K. Qin, ¡°Graph convolutional networks with multi-level coarsening for graph classification¡±, Knowledge-Based Systems, 194: Article No. 105578, 2020. (SCI IF: 8.8; Q1)

  55. N. Tran, J.-G. Schneider, I. Weber and A. K. Qin, ¡°Hyper-parameter optimization in classification: To-do or not-to-do¡±, Pattern Recognition, 103:107245, 2020. (SCI IF: 8.0; Q1)

  56. Y. Xie, M. G. Gong, Y. Gao, A. K. Qin and X. L. Fan, ¡°A multi-task representation learning architecture for enhanced graph classification¡±, Frontiers in Neuroscience, 13: Article No. 1395, 2020. (SCI IF: 4.3; Q2)

  57. M. G. Gong, Y. Xie, K. Pan, K. Y. Feng and A. K. Qin, ¡°A survey on differentially private machine learning¡±, IEEE Computational Intelligence Magazine, 15(2): 49-64, 2020. (SCI IF: 9.0; Q1)

  58. X. L. Zheng, A. K. Qin, M. G. Gong and D. Y. Zhou, ¡°Self-regulated evolutionary multi-task optimization¡±, IEEE Transactions on Evolutionary Computation, 24(1): 16-28, 2020. (SCI IF: 14.3; Q1)

  59. J. Liu, M. G. Gong, A. K. Qin and K. C. Tan, ¡°Bipartite differential neural network for unsupervised image change detection¡±, IEEE Transactions on Neural Networks and Learning Systems, 31(3): 876-890, 2020. (SCI IF: 10.4; Q1)

  60. G. Q. Li, F. Zeng, H. Q. Li and A. K. Qin, ¡°Matrix function optimization problems under orthonormal constraint¡±, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3): 802-814, 2020. (SCI IF: 8.7; Q1)

  61. H. Song, A. K. Qin and F. D. Salim, ¡°Evolutionary model construction for electricity consumption prediction¡±, Neural Computing and Applications, 33: 12155-12172, 2020. (SCI IF: 6.0; Q1)

  62. Y. Xie, M. G. Gong, A. K. Qin, Z. D. Tang and X. L. Fan, ¡°TPNE: Topology preserving network embedding¡±, Information Sciences, 504: 20-31, 2019. (SCI IF: 8.1; Q1)

  63. L. Feng, L. Zhou, J. Zhong, A. Gupta, Y. S. Ong, K. C. Tan and A. K. Qin, ¡°Evolutionary multitasking via explicit autoencoding¡±, IEEE Transactions on Cybernetics, 49(9): 3457-3470, 2019. (SCI IF: 11.8; Q1)

  64. C. Jacobs, T. Collett, K. Glazebrook, E. Buckley-Geer, H. T. Dieh, H. Lin, C. McCarthy, A. K. Qin, et al., ¡°An extended catalog of galaxy¨Cgalaxy strong gravitational lenses discovered in des using convolutional neural networks¡±, Astrophysical Journal Supplement Series, 243(1): Article No. 17, 2019. (SCI IF: 8.7; Q1)

  65. X. L. Liang, A. K. Qin, K. Tang and K. C. Tan, ¡°QoS-aware web service composition with internal complementarity¡±, IEEE Transactions on Services Computing, 12(2): 276-289, 2019. (SCI IF: 8.1; Q1)

  66. C. Jacobs, T. Collett, K. Glazebrook, C. McCarthy, A. K. Qin, et al. ¡°Finding high-redshift strong lenses in DES using convolutional neural networks¡±, Monthly Notices of the Royal Astronomical Society, 484(4): 5330-5349, 2019. (SCI IF: 4.8; Q1)

  67. B. Kazimipour, M. N. Omidvar, A. K. Qin, X. D. Li and X. Yao, ¡°Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems¡±, Applied Soft Computing, 76: 265-281, 2019. (SCI IF: 8.7; Q1)

  68. J. Liono, P. P. Jayaramanb, A. K. Qin, T. Nguyen and F. D. Salim, ¡°QDaS: Quality driven data summarization for effective storage management in Internet of Things¡±, Journal of Parallel and Distributed Computing, 127: 196-208, 2019. (SCI IF: 3.8; Q1)

  69. J. Liu, M. G. Gong, A. K. Qin and P. Zhang, ¡°A deep convolutional coupling network for change detection based on heterogeneous optical and radar images¡±, IEEE Transactions on Neural Networks and Learning Systems, 29(3): 545-559, 2018. (SCI IF: 10.4; Q1)

  70. S. K. Mistry, A. Bouguettaya, H. Dong and A. K. Qin, ¡°Metaheuristic optimization for long-term IaaS service composition¡±, IEEE Transactions on Services Computing, 11(1): 131-143, 2018. (SCI IF: 8.1; Q1)

  71. L. S. Cui, X. Z. Zhang, A. K. Qin, T. Sellis and L. F. Wu, ¡°CDS: Collaborative distant supervision for Twitter account classification¡±, Expert Systems with Applications, 83: 94-103, 2016. (SCI IF: 8.5; Q1)

  72. C. Zhang, P. Lim, A. K. Qin and K. C. Tan, ¡°Multi-objective deep belief networks ensemble for remaining useful life estimation in prognostics¡±, IEEE Transactions on Neural Networks and Learning Systems, 28(10): 2306-2318, 2016. (SCI IF: 10.4; Q1)

  73. K. Y. Lu, S. Y. Xia, J. K. Zhang, A. K. Qin, ¡°Robust road detection in shadow conditions¡±, Journal of Electronic Imaging, 25(4): Article No. 043027, 2016. (SCI IF: 1.1; Q3)

  74. B. Y. Qu, B. F. Lang, J. J. Liang, A. K. Qin and O. D. Crisalle, ¡°Two-hidden-layer extreme learning machine for regression and classification¡±, Neurocomputing, 175: 826-834, 2016. (SCI IF: 6.0; Q1)

  75. M. G. Gong, Y. Wu, Q. Cai, W. Ma, A. K. Qin, Z. Wang and L. Jiao, ¡°Discrete particle swarm optimization for high-order graph matching¡±, Information Sciences, 328: 158-171, 2016. (SCI IF: 8.1; Q1)

  76. L. Wan, K. Tang, M. Li, Y. Zhong and A. K. Qin, ¡°Collaborative active and semi-supervised learning for hyperspectral remote sensing image classification¡±, IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2384-2396, 2015. (SCI IF: 8.2; Q1)

  77. Y. Xiao, Z.-Y. Wang, J. Li, Z.-L. Yuan and A. K. Qin, ¡°Two-step beveled UWB printed monopole antenna with band-notch¡±, International Journal of Antennas and Propagation, 2014: Article No. 173704, 2014. (SCI IF: 1.5; Q3)

  78. S. J. Guo, S.-U. Guan, W. F. Li, K. L. Man, F. Liu and A. K. Qin, ¡°Input space partitioning for neural network learning¡±, International Journal of Applied Evolutionary Computation, 4(2): 56-66, 2013.

  79. P. Yu, A. K. Qin and D. A. Clausi, ¡°Feature extraction of dual-pol SAR imagery for sea-ice image segmentation¡±, Canadian Journal of Remote Sensing, 38(3): 352-366, 2012. (SCI IF: 2.6; Q2)

  80. P. Yu, A. K. Qin and D. A. Clausi, ¡°Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty¡±, IEEE Transactions on Geoscience and Remote Sensing, 50(4): 1302-1317, 2012. (SCI IF: 8.2; Q1)

  81. A. K. Qin and D. A. Clausi, ¡°Multivariate image segmentation based on semantic region growing with adaptive edge penalty¡±, IEEE Transactions on Image Processing, 19(8): 2157-2170, 2010. (SCI IF: 10.6; Q1)

  82. D. A. Clausi, A. K. Qin, M. S. Chowdhury, P. Yu and P. Maillard, ¡°MAGIC: MAp-guided ice classification system¡±, Canadian Journal of Remote Sensing, 36(1): 13-25, 2010. (SCI IF: 2.6; Q2)

  83. A. K. Qin, V. L. Huang and P. N. Suganthan, ¡°Differential evolution with strategy adaptation for global numerical optimization¡±, IEEE Transactions on Evolutionary Computation, 13(2): 398- 417, 2009. (SCI IF: 14.3; Q1)

  84. J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, ¡°Comprehensive learning particle swarm optimizer for global optimization of multimodal functions¡±, IEEE Transactions on Evolutionary Computation, 10(3): 281- 295, 2006. (SCI IF: 14.3; Q1)

  85. A. K. Qin, P. N. Suganthan and M. Loog, ¡°Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction¡±, Pattern Recognition, 39(9): 1805-1808, 2006. (SCI IF: 8.0; Q1)

  86. J. J. Liang, S. Baskar, P. N. Suganthan and A. K. Qin, ¡°Performance evaluation of multiagent genetic algorithm¡±, Natural Computing, 5(1): 83-96, 2006. (SCI IF: 2.1; Q3)

  87. Q.-Y. Zhu, A. K. Qin, P. N. Suganthan and G.-B. Huang, ¡°Evolutionary extreme learning machine¡±, Pattern Recognition, 38(10): 1759-1763, 2005. (SCI IF: 8.0; Q1)

  88. A. K. Qin and P. N. Suganthan, ¡°Enhanced neural gas network for prototype based clustering¡±, Pattern Recognition, 38(8): 1275-1288, 2005. (SCI IF: 8.0; Q1)

  89. A. K. Qin and P. N. Suganthan, ¡°Initialization insensitive LVQ algorithm based on dynamic cost function adaptation¡±, Pattern Recognition, 38(5): 774-776, 2005. (SCI IF: 8.0; Q1)

  90. A. K. Qin, P. N. Suganthan and M. Loog, ¡°Uncorrelated heteroscedastic LDA algorithm based on the weighted pairwise Chernoff criterion¡±, Pattern Recognition, 38(4): 613-616, 2005. (SCI IF: 8.0; Q1)

  91. E. K. Tang, P. N. Suganthan, X. Yao and A. K. Qin, ¡°Linear dimensionality reduction using relevance weighted LDA¡±, Pattern Recognition, 38(4): 485-493, 2005. (SCI IF: 8.0; Q1)

  92. A. K. Qin and P. N. Suganthan, ¡°Robust growing neural gas algorithm with application in cluster analysis¡±, Neural Networks, 17 (8-9): 1135-1148, 2004. (SCI IF: 7.8; Q1)

Referred Conference Proceedings

  1. B. Wang, A. K. Qin, S. Shafiei, H. Dia, S. Mihaita and H. Grzybowska, ¡°Training physics-informed neural networks via multi-task optimisation for traffic density prediction¡±, Proc. of the 2023 IEEE International Joint Conference on Neural Networks (IJCNN¡¯23), Gold Coast, Queensland, Australia, June 18-23, 2023.

  2. B. Y. Hu, Y. Sun and A. K. Qin, ¡°A general multiple data augmentation framework for training deep neural networks¡±, Proc. of the 2022 IEEE International Joint Conference on Neural Networks (IJCNN¡¯22), Padua, Italy, July 18-23, 2022.

  3. H. Song, A. K. Qin and C. G. Yan, ¡°Multi-task optimization based co-training for electricity consumption prediction¡±, Proc. of the 2022 IEEE International Joint Conference on Neural Networks (IJCNN¡¯22), Padua, Italy, July 18-23, 2022.

  4. D. Tedjopurnomo, Z. F. Bao, F. Choudhury, H. Luo and A. K. Qin, ¡°Equitable public bus network optimization for social good: A case study of Singapore¡±, Proc. of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT¡¯22), Seoul Republic of Korea, June 21-24, 2022.

  5. D. Zhao, A.-S. Mihaita, Y. M. Ou, S. Shafiei, H. Grzybowska, K. Qin, G. Tan, M. Li and H. Dia, ¡°Traffic disruption modelling with mode shift in multi-modal networks¡±, Proc. of the 25th IEEE International Conference on Intelligent Transportation Systems (ITSC¡¯22), Macau, China, October 8-12, 2022.

  6. S. Nag, Y. Khandelwal, S. Mittal, C. K. Mohan and A. K. Qin, ¡°ARCN: A real-time attention-based network for crowd counting from drone images¡±, Proc. of the IEEE 18th India Council International Conference (INDICON¡¯21), Guwahati, India, December 19-21, 2021

  7. R. J. Duan, Y. F. Cheng, D. T. Niu, Y. Yang, A. K. Qin and Y. He, ¡°AdvDrop: Adversarial Attack to DNNs by Dropping Information¡±, Proc. of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV¡¯21), October 11-17, 2021.

  8. N. Sultana, J. Chan, T. Sarwar and A. K. Qin, ¡°Learning to optimise routing problems using policy optimisation¡±, Proc. of the 2021 IEEE International Joint Conference on Neural Networks (IJCNN¡¯21), Shenzhen, China, July 18-22, 2021.

  9. X. Zhou, A. K. Qin, Y. N. Sun and K. C. Tan, ¡°A survey of advances in evolutionary neural architecture search¡±, Proc. of the 2021 IEEE Congress on Evolutionary Computation (CEC¡¯21), Krak¨®w, Poland, 28 June - 1 July 2021.

  10. R. J. Duan, X. F. Mao, A. K. Qin, Y. F. Chen. S. K. Ye, Y. He and Y. Yang, ¡°Adversarial laser beam: Effective physical-world attack to DNNs in a blink¡±, Proc. of the 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2021), Nashville, Tennessee, USA, June 19-25, 2021.

  11. Z. Q. Li, H. Pan, Y. P. Zhu and A. K. Qin, ¡°PGD-UNet: A position-guided deformable network for simultaneous segmentation of organs and tumors¡±, Proc. of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN¡¯20), Glasgow, UK, July 19-24, 2020.

  12. B. Y. Zhang, A. K. Qin, H. Pan and T. Sellis, ¡°A novel DNN training framework via data sampling and multi-task optimization¡±, Proc. of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN¡¯20), Glasgow, UK, July 19-24, 2020.

  13. P. Abeysekara, H. Dong and A. K. Qin, ¡°Distributed machine learning for predictive analytics in mobile edge computing based IoT environments¡±, Proc. of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN¡¯20), Glasgow, UK, July 19-24, 2020.

  14. R. J. Duan, X. J. Ma, Y. S. Wang, J. Bailey, A. K. Qin and Y. Yang, ¡°Adversarial camouflage: Hiding adversarial examples with natural styles¡±, Proc. of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR¡¯20), Seattle, Washington, USA, June 16-18, 2020.

  15. Y. Q. Ren, Y. Q. Sun, D. Wu, Z. H. Cui and A. K. Qin, ¡°A new makeup transfer with super-resolution¡±, Proc. of the 26th International Conference on Neural Information Processing (ICONIP¡¯19), Sydney, Australia, December 12-15, 2019.

  16. Z. M. Liu, G. Y. Gao, A. K. Qin, T. Wu and C. H. Liu, ¡°Action recognition with bootstrapping based long-range temporal context attention¡±, Proc. of the 27th ACM International Conference on Multimedia (ACMMM 2019), Nice, France, October 21-25, 2019.

  17. P. Abeysekara, H. Dong and A. K. Qin, ¡°Machine learning-driven trust for MEC-based IoT services¡±, Proc. of the 2019 IEEE Conference on Web Services (ICWS'19), Milan, Italy, July 8-13, 2019.

  18. H. Song, A. K. Qin, P. W. Tsai and J. J. Liang, ¡°Multitasking multi-swarm optimization¡±, Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC¡¯19), Wellington, New Zealand, June 10-13, 2019.

  19. C. Jin, P. W. Tsai and A. K. Qin, ¡°A study on knowledge reuse strategies in multitasking differential evolution¡±, Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC¡¯19), Wellington, New Zealand, June 10-13, 2019.

  20. X. L. Zheng, Y. Lei, A. K. Qin, D. Y. Zhou, J. Shi and M. G. Gong, "Differential evolutionary multi-task optimization¡±, Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC¡¯19), Wellington, New Zealand, June 10-13, 2019.

  21. J. Liono, F. D. Salim, N. V. Berkel, V. Kostakos and A. K. Qin, ¡°Improving experience sampling with multi-view user-driven annotation prediction¡±, Proc. of the 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom¡¯19), Kyoto, Japan, March 10 - 15, 2019.

  22. J. Liono, Z. S. Abdallah, A. K. Qin and F. D. Salim, ¡°Inferring transportation mode and human activity from mobile sensing in daily life¡±, Proc. of the 15th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous¡¯18), New York, NY, USA, November 5-7, 2018.

  23. B. Y. Zhang, A. K. Qin and T. Sellis, ¡°Evolutionary feature subspaces generation for ensemble classification¡±, Proc. of the Genetic and Evolutionary Computation Conference (GECCO¡¯18), Kyoto, Japan, July 15-19, 2018.

  24. H. Song, A. K. Qin and F. D. Salim, ¡°Evolutionary multi-objective ensemble learning for multivariate electricity consumption prediction¡±, Proc. of the 2018 IEEE International Joint Conference on Neural Networks (IJCNN¡¯18), Rio de Janeiro, Brazil, 2018.

  25. B. Y. Zhang, A. K. Qin and J. Chan, ¡°An efficient binary search based neuron pruning method for ConvNet condensation¡±, Proc. of the 24th International Conference on Neural Information Processing (ICONIP¡¯17), Guangzhou, China, November 14-18, 2017.

  26. H. Song, A. K. Qin and F. D. Salim, ¡°Multi-resolution selective ensemble extreme learning machine for electricity consumption prediction¡±, Proc. of the 24th International Conference on Neural Information Processing (ICONIP¡¯17), Guangzhou, China, November 14-18, 2017.

  27. S. Jayaratna, A. Bouguettaya, H. Dong, A. K. Qin and A. Erradi, ¡°Subjective evaluation of market-driven cloud services¡±, Proc. of the 24th IEEE International Conference on Web Services (ICWS¡¯17), Hawaii, USA, June 25-30, 2017.

  28. C. Jin and and A. K. Qin, ¡°A GPU-based implementation of brain storm optimization¡±, Proc. of the 2017 IEEE Congress on Evolutionary Computation (CEC¡¯17), Donostia-San Sebasti¨¢n, Spain, June 5-8, 2017.

  29. Jonathan Liono, F. D. S. Salim and A. K. Qin ¡°Optimal time window for temporal segmentation of sensor streams in multi-activity recognition¡±, Proc. of the 13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous¡¯16), Hiroshima, Japan, November 28-December 1, 2016.

  30. J. K. Zhang, S. Y. Xia, H. Pan and A. K. Qin, ¡°A genetics-motivated unsupervised model for tri-subject kinship verification¡±, Proc. of the 23rd IEEE International Conference on Image Processing (ICIP'16), Phoenix, Arizona, USA, September 25¨C28, 2016.

  31. J. K. Zhang, S. Y. Xia, K. Y. Lu, H. Pan and A. K. Qin, ¡°Robust road detection from a single image¡±, Proc. of the 23rd International Conference on Pattern Recognition (ICPR'16), Cancun, Mexico, December 4-8, 2016.

  32. A. Campbell, V. Ciesielski and A. K. Qin, ¡°Node label matching improves classification performance in deep belief networks¡±, Proc. of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN'16), Vancouver, Canada, July 25-29, 2016.

  33. Y. W. Li, L. Z. Jin, A. K. Qin, C. Y. Sun, Y. S. Ong and T. Cui, ¡°Semi-supervised auto-encoder based on manifold learning¡±, Proc. of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN'16), Vancouver, Canada, July 25-29, 2016.

  34. H. Song, A. K. Qin and F. D. Salim, ¡°Multivariate electricity consumption prediction with extreme learning machine¡±, Proc. of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN'16), Vancouver, Canada, July 25-29, 2016.

  35. A. Kenny, X. D. Li, A. K. Qin and A. T. Ernst, ¡°A population-based local search technique with random descent and jump for the steiner tree problem in graphs¡±, Proc. of the 2016 Genetic and Evolutionary Computation Conference (GECCO¡¯16), Denver, Colorado, USA, July 20-24, 2016.

  36. R. Chen, H. R. Li, A. K. Qin, S. Kasiviswanathan and H. X. Jin, ¡°Private spatial data aggregation in the local setting¡±, Proc. of the 32nd IEEE International Conference on Data Engineering (ICDE'16), Helsinki, Finland, May 16-20, 2016.

  37. S. Xia, J. K. Zhang, K. Y. Lu and A. K. Qin, ¡°Road detection via unsupervised feature learning¡±, Proc. of the 30th International Conference on Image and Vision Computing New Zealand (IVCNZ'15), Auckland, New Zealand, November 23-24, 2015.

  38. S. Mistry, A. Bouguettaya, H. Dong and A. K. Qin, ¡°Optimizing Long-term IaaS Service Composition¡±, Proc. of the 13th International Conference on Service Oriented Computing (ICSOC¡¯15), Goa, India, November 16-19, 2015.

  39. S. Mistry, A. Bouguettaya, H. Dong and A. K. Qin, ¡°Predicting dynamic requests behavior in long-term IaaS service composition¡±, Proc. of the 22nd IEEE International Conference on Web Services (ICWS¡¯15), New York, USA, June 27 - July 2, 2015.

  40. B. Kazimipour, M. Omidvar, X. Li and A. K. Qin, ¡°A sensitivity analysis of contribution-based cooperative co-evolutionary algorithms¡±, Proc. of the 2015 IEEE Congress on Evolutionary Computation (CEC¡¯15), Sendai, Japan, May 25-28, 2015.

  41. S. C. Liu, Y. F. Wei, K. Tang, A. K. Qin and X. Yao, ¡°QoS-aware long-term based service composition in cloud computing¡±, Proc. of the 2015 IEEE Congress on Evolutionary Computation (CEC¡¯15), Sendai, Japan, May 25-28, 2015.

  42. C. Jin, A. K. Qin and K. Tang, ¡°Local ensemble surrogate assisted crowding differential evolution¡±, Proc. of the 2015 IEEE Congress on Evolutionary Computation (CEC¡¯15), Sendai, Japan, 2015.

  43. Allan Campbell, Vic Ciesielski and A. K. Qin, ¡°Feature discovery by deep learning for aesthetic analysis of evolved abstract images¡±, Proc. of Evostar 2015, Copenhagen, Denmark, April 8-10, 2015.

  44. B. Kazimipour, X. Li and A. K. Qin, ¡°Why advanced population initialization techniques perform poorly in high dimensions?¡± Proc. of the 10th International Conference on Simulated Evolution and Learning (SEAL'14), Dunedin, New Zealand, December 15-18, 2014.

  45. P. Yang, K. Tang, L. Li and A. K. Qin, ¡°Evolutionary robust optimization with multiple solutions¡±, Proc. of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES¡¯14), Singapore, November 10-12, 2014.

  46. T. H. Wong, A. K. Qin, S. Wang and Y. Shi, ¡°cuSaDE: A CUDA-based parallel self-adaptive differential evolution algorithm¡±, Proc. of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES¡¯14), Singapore, November 10-12, 2014.

  47. J. J. Liang, H. Song, B. Y. Qu, W. Liu and A. K. Qin, ¡°Neural network based on dynamic multi-swarm particle swarm optimizer for ultra-short-term load forecasting¡±, Proc. of the 5th International Conference on Swarm Intelligence (ICSI'14), Hefei, China, October 17-20, 2014.

  48. A. K. Qin, K. Tang, Hong Pan and Si-Yu Xia, ¡°Self-adaptive differential evolution with local search chains for real-parameter single-objective optimization¡±, Proc. of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, July 6-11, 2014.

  49. B. Xue, A. K. Qin and M. Zhang, ¡°An archive based particle swarm optimisation for feature selection in classification¡±, Proc. of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, July 6-11, 2014.

  50. B. Kazimipour, Xiaodong Li, A. K. Qin, ¡°A review of population initialization techniques for evolutionary algorithms¡±, Proc. of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, July 6-11, 2014.

  51. B. Kazimipour, M. N. Omidvar, Xiaodong Li, A. K. Qin, ¡°A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization¡±, Proc. of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, July 6-11, 2014.

  52. B. Kazimipour, Xiaodong Li and A. K. Qin, ¡°Effects of population initialization on differential evolution for large scale optimization¡±, Proc. of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, July 6-11, 2014.

  53. J. Zhong, K. Tang and A. K. Qin, ¡°Finding convex hull vertices in metric space¡±, Proc. of the 2014 IEEE International Joint Conference on Neural Networks (IJCNN'14), Beijing, China, July 6-11, 2014.

  54. Si-Yu Xia, Hong Pan and A. K. Qin, ¡°Face clustering in photo album¡±, Proc. of the 22nd International Conference on Pattern Recognition (ICPR'14), Stockholm, Sweden, August 24-28, 2014.

  55. W. Liu, H. Song, J. J. Liang, B. Y. Qu, A. K. Qin, ¡°Neural network based on self-adaptive differential evolution for ultra-short-term power load forecasting¡±, Proc. of the 10th International Conference on Intelligent Computing (ICIC'14), Taiyuan, China, August 3-6, 2014.

  56. A. K. Qin and Xiaodong Li, ¡°Differential evolution on the CEC-2013 single-objective continuous optimization testbed¡±, Proc. of the 2013 IEEE Congress on Evolutionary Computation (CEC'13), Cancun, Mexico, June 20-23, 2013.

  57. Feng Xie, A. K. Qin, Andy Song and Vic Ciesielski, ¡°Sensor-based activity recognition with improved GP-based classifier¡±, Proc. of the 2013 IEEE Congress on Evolutionary Computation (CEC'13), Cancun, Mexico, June 20-23, 2013.

  58. Borhan Kazimipour, Xiaodong Li and A. K. Qin, ¡°Initialization methods for large scale global optimization¡±, Proc. of the 2013 IEEE Congress on Evolutionary Computation (CEC'13), Cancun, Mexico, June 20-23, 2013.

  59. A. K. Qin and Xiaodong Li, ¡°Investigation of self-adaptive differential evolution on the CEC-2013 single-objective continuous optimization testbed¡±, Proc. of the 2013 IEEE Congress on Evolutionary Computation (CEC'13), Cancun, Mexico, June 20-23, 2013.

  60. Hong Pan, Yaping Zhu, A. K. Qin and Liangzheng Xia, ¡°Mining heterogeneous class-specific codebook for categorical object detection and classification¡±, Proc. of the 2013 IEEE International Conference on Image Processing (ICIP'13), Melbourne, Australia, September 15-18, 2013.

  61. Hong Pan, Yaping Zhu, Si-Yu Xia and K. Qin, ¡°Improved generic categorical object detection Fusing depth cue with 2D appearance and shape features¡±, Proc. of the 21st International Conference on Pattern Recognition (ICPR'12), Tsukuba Science City, Japan, November, 2012.

  62. A. K. Qin, F. Raimondo, F. Forbes and Y. S. Ong, ¡°An improved CUDA-based implementation of differential evolution on GPU¡±, Proc. of the 2012 Genetic and Evolutionary Computation Conference (GECCO'12), Philadelphia, USA, July, 2012.

  63. A. K. Qin and F. Forbes, ¡°Dynamic regional harmony search with opposition and local learning¡±, Proc. of the 2011 Genetic and Evolutionary Computation Conference (GECCO'11), Dublin, Ireland, July, 2011.

  64. A. K. Qin and F. Forbes, ¡°Harmony search with differential mutation based pitch adjustment¡±, Proc. of the 2011 Genetic and Evolutionary Computation Conference (GECCO'11), Dublin, Ireland, July, 2011.

  65. D. A. Clausi, A. K. Qin, M. S. Chowdhury, P. Yu and P. Maillard, ¡°MAGIC: MAp-Guided Ice Classification system for operational analysis¡±, Proc. of the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS¡¯08) in conjunction with the 19th International Conference on Pattern Recognition (ICPR¡¯08), Tampa, Florida, USA, December 7, 2008.

  66. V. L. Huang, A. K. Qin and P. N. Suganthan, ¡°Multi-objective optimization based on self-adaptive differential evolution algorithm¡±, Proc. of IEEE Congress on Evolutionary Computation (CEC¡¯07), Singapore, September 2007.

  67. A. K. Qin, P. N. Suganthan, C. H. Tay and H. S. Pa, ¡°Personal identification system based on multiple palmprint features¡±, Proc. of the 9th International Conference on Control, Automation, Robotics and Vision (ICARCV¡¯06), Singapore, December, 2006.

  68. A. K. Qin, P. N. Suganthan and M. Loog, ¡°Efficient feature extraction based on regularized uncorrelated Chernoff discriminant analysis¡±, Proc. of the 18th Int. Conf. on Pattern Recognition (ICPR¡¯06), Hong Kong, August 2006.

  69. V. L. Huang, A. K. Qin and P. N. Suganthan, ¡°Self-adaptive differential evolution algorithm for constrained real-parameter optimization¡±, Proc. of IEEE Congress on Evolutionary Computation (CEC¡¯06), Vancouver, BC, Canada, July 2006.

  70. A. K. Qin and P. N. Suganthan, ¡°Self-adaptive differential evolution algorithm for numerical optimization¡±, Proc. of the 2005 IEEE Congress on Evolutionary Computation (CEC¡¯05), Edinburgh, UK, September 2005.

  71. A. K. Qin, S. Y. M. Shi, P. N. Suganthan and M. Loog, ¡°Enhanced direct linear discriminant analysis for feature extraction on high dimensional data¡±, Proc. of the 20th National Conference on Artificial Intelligence (AAAI¡¯05), Pittsburgh, Pennsylvania, USA, July 2005.

  72. J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, ¡°Evaluation of comprehensive learning particle swarm optimizer¡±, Proc. of the 11th International Conference on Neural Information Processing (ICONIP¡¯04), pp. 230-235, Science City, Calcutta, November 2004.

  73. J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, ¡°Particle swarm optimization algorithms with novel learning strategies¡±, Proc. of IEEE International Conference on systems, man and cybernetics 2004 (SMC¡¯04), Hague, Netherlands, October 2004.

  74. A. K. Qin, P. N. Suganthan and J. J. Liang, ¡°A new generalized LVQ algorithm via harmonic to minimum distance measure transition¡±, Proc. of IEEE International Conference on systems, man and cybernetics 2004 (SMC¡¯04), Hague, Netherlands, October 2004.

  75. A. K. Qin and P. N. Suganthan, ¡°A novel kernel prototype-based learning algorithm¡±, Proc. of the 17th International Conference on Pattern Recognition (ICPR¡¯04), Cambridge, UK, August 2004.

  76. A. K. Qin and P. N. Suganthan, ¡°Kernel neural gas algorithms with application to cluster analysis¡±, Proc. of the 17th International Conference on Pattern Recognition (ICPR¡¯04), Cambridge, UK, August 2004.

  77. A. K. Qin and P. N. Suganthan, ¡°Growing generalized learning vector quantization with local neighborhood adaptation rule¡±, Proc. of IEEE International Conference on Intelligent Systems (ICIS¡¯04), Sofia, Bulgaria, June 2004.

  78. A. K. Qin and P. N. Suganthan, ¡°A robust neural gas algorithm for clustering analysis¡±, Proc. of the International Conference on Intelligent Sensing and Information Processing (ICISIP¡¯04), Chennai, India, January 2004.

Technical Reports

  1. B. Da, Y. S. Ong, L. Feng, A. K. Qin, A. Gupta, Z. Zhu, C. K. Ting, K. Tang, X. Yao, "Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results", arXiv preprint arXiv:1706.03470, 2017.

  2. Y. Yuan, Y. S. Ong, L. Feng, A. K. Qin, A. Gupta, B. Da, Q. Zhang, K. C. Tan, Y. Jin, "Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results", arXiv preprint arXiv:1706.02766, 2017.

  3. X. Li, K. Tang, M. Omidvar, Z. Yang and K. Qin, "Benchmark Functions for the CEC'2013 Special Session and Competition on Large Scale Global Optimization", Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013.

  4. V. L. Huang, A. K. Qin, K. Deb, E. Zitzler, P. N. Suganthan, J. J .Liang, M. Preuss and S. Huband, "Problem definitions for performance assessment of multi-objective optimization algorithms", Special Session & Competition on Performance Assessment of Multi-Objective Optimization Algorithms for IEEE Congress on Evolutionary Computation (CEC¡¯07), Singapore, Technical Report, Nanyang Technological University, Singapore, 2007.