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My Erdös Number is at most 4; my Einstein Number is at most 6.

(last updated: 04 June 2026). Publication in total: , of which peer reviewed: .

Research Area Tags
ML Machine Learning CV Computer Vision Evo Evolutionary Computation AIS Artificial Immune Systems Bio Bio-inspired Computing Swarm Swarm Intelligence Bioinfo Bioinformatics SysBio Systems Biology QR Qualitative Reasoning LLM Large Language Models XAI Explainable AI Opt Optimisation Health Health & Medicine

Journal Publications ()

  1. Mao, Y., Li, H., Pang, W., Papanastasiou, G., Yang, G., Wang, C. (2026). SeLoRA: Self-expanding LoRA for high-quality and efficient medical image synthesis. Expert Systems with Applications. DOI ESWA
  2. Li, B., Ding, B., Pang, W., Ni, H. (2026). Triple-Flow Dynamic Graph Convolutional Network for Wind Power Forecasting. Symmetry, Vol 17(12). DOI
  3. Yang, Z., Xu, D., Pang, W., Yuan, Y. (2025). Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models. Transactions on Machine Learning Research. OpenReview arXiv TMLR
  4. Gherman, M., Sharma, K., Rees-Garbutt, J., Pang, W., Abdallah, Z., Marucci, L. (2025). Accelerated design of Escherichia coli reduced genomes using a whole-cell model and machine learning. Cell Systems. DOI Code
  5. Liu, Y., Ai, S., Zhou, Z., Pang, W., Dong, C., Ge, H., Liao, D., Huang, Y. (2025). Image Copyright Dual-Protection Based on Extractable and Imperceptible Adversarial Watermark. Big Data Mining and Analytics, Accepted. BDMA
  6. Yu, X., Ding, L., Zhang, D., Wu, J., Liang, M., Zheng, J., Pang, W. (2025). Decoupled pixel-wise correction for abdominal multi-organ segmentation. Complex & Intelligent Systems. DOI Code CAIS
  7. Chi, M., Pang, W., Wu, X., Zhao, P., Li, Y., Wang, T., Qian, J., Xiao, Y., Wang, L., Zhou, Y. (2025). A Generalized Neural Solver based on LLM-Guided Heuristic Evolution framework for Solving Diverse Variants of Vehicle Routing Problems. Expert Systems with Applications, Vol 291. DOI ESWA
  8. Wang, D., Pang, W., Cao, Z., Song, L., An, L., Wu, X., Zhao, P., Wang, L., Zhou, Y. (2025). A Lightweight Model LGCSPNet for Sitting Posture Risk Management Applications. Expert Systems with Applications, Vol 291. DOI ESWA
  9. Wang, C., Zhou, Y., Li, Y., Pang, W., Wang, L., Du, W., Yang, H., Jin, Y. (2025). ICPPNet: A semantic segmentation network model based on inter-class positional prior for scoliosis reconstruction in ultrasound images. Journal of Biomedical Informatics. DOI JBI
  10. Hu, Y., Rao, Y., Yu, H., Wang, G., Fan, H., Pang, W., Dong, J. (2025). Out-of-Distribution Monocular Depth Estimation with Local Invariant Regression. Knowledge-Based Systems. DOI KBS
  11. Yu, X., Teng, L., Duang, Z., Zhang, D., Pang, W., Liang, M., Zheng, J., Qiu, L., Xu, Q. (2025). Bias-Variance Decomposition Knowledge Distillation for Medical Image Segmentation. Neurocomputing. DOI Neurocomputing
  12. Wang, Y., Pang, W., Wang, D., Pedrycz, W. (2025). One-shot Federated K-means Clustering based on Density Cores. IEEE Transactions on Neural Networks and Learning Systems, Vol 36, Issue 8. Preprint DOI IEEE TNNLS
  13. Li, Y., Hong, K., Shi, X., Pang, W., Xiao, Y., Zhao, P., Xu, D., Song, C., Zhou, X., Zhou, Y. (2025). A deep learning based approach for automatic cardiac events identification. Biomedical Signal Processing and Control, 100(Part 2), Article 107164. DOI
  14. Hassan, M., Lin, M., Fateh, A., Pang, W., Zhang, L., Wang, D., Yun, G., Zeng, H. (2024). Attention Over Vulnerable Brain Regions Associating Cerebral Palsy Disorder and Biological Markers. Journal of Advanced Research. DOI JAR
  15. Xiao, Y., Wang, D., Li, B., Chen, H., Pang, W., Wu, X., Li, H., Xu, D., Liang, Y., Zhou, Y. (2024). Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems. IEEE Transactions on Neural Networks and Learning Systems, Vol 36, Issue 7. DOI arXiv IEEE TNNLS
  16. Song, J., Yuan, Y., Chang, K., Xu, B., Xuan, J., Pang, W. (2024). Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation. Energy and AI, Vol 18. arXiv DOI
  17. Wang, Y., Pang, W., Pedrycz, W. (2024). One-Shot Federated Clustering Based on Stable Distance Relationships. IEEE Transactions on Industrial Informatics, Vol 20, Issue 11. DOI IEEE TII
  18. Hu, R., Wang, X., Ding, X., Zhang, Y., Xin, X., Pang, W., Yu, S. (2024). Unsupervised Domain Adaptation for Skeleton Recognition with Fourier Analysis. IEEE Internet of Things Journal, Vol 11, Issue 24. DOI IEEE IOT
  19. Hassan, M., Wang, Y., Wang, D., Pang, W., Li, D., Zhou, Y., Xu, D., et al. (2024). Deep learning model for human-intuitive shoeprint reconstruction. Expert Systems with Applications, Vol 249. DOI ESWA
  20. Huang, L., Bai, X., Zeng, J., Yua, M., Pang, W., Wang, K. (2024). FAM: Improving Columnar Vision Transformer with Feature Attention Mechanism. Computer Vision and Image Understanding, Vol 242. DOI CVIU
  21. Sun, M., Hu, H., Pang, W., Zhou, Y. (2023). ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information. International Journal of Molecular Sciences, Vol 24, No 20, 15447. DOI IJMS
  22. Hassan, M., Zhang, H., Fateh, A.A., Ma, S., Liang, W., Shang, D., Deng, J., Zhang, Z., Lam, T., Xu, M., Huang, Q., Yu, D., Zhang, C., Zhou, Y., Pang, W., Yang, C., Qin, P. (2023). Retinal disease projection conditioning by biological traits. Complex & Intelligent Systems. DOI CAIS
  23. Hou, W., Wang, Y., Zhao, Z., Cong, Y., Pang, W., Tian, Y. (2023). Hierarchical Graph Neural Network with Subgraph Perturbations for Key Gene Cluster Discovery in Cancer Staging. Complex & Intelligent Systems, Vol 10, pp. 111–128. DOI CAIS
  24. Awad, A., Coghill, G.M., Pang, W. (2023). A novel Physarum-inspired competition algorithm for discrete multi-objective optimisation problems. Soft Computing, Vol 27, pp. 14699–14719. DOI
  25. Gherman, I., Abdallah, Z., Pang, W., Gorochowski, T., Grierson, C., Marucci, L. (2023). Bridging the gap between mechanistic biological models and machine learning surrogates. PLOS Computational Biology. DOI
  26. Huang, L., Sun, S., Zeng, J., Wang, W., Pang, W., Wang, K. (2023). U-DARTS: Uniform-space differentiable architecture search. Information Sciences, Vol 628, pp. 339–349. DOI
  27. Cong, Y., Wang, Y., Hou, W., Pang, W. (2023). Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching. Entropy, Vol 25(1), 106. DOI
  28. Li, B., Lu, Y., Pang, W., Xu, H. (2023). Image Colorization using CycleGAN with semantic and spatial rationality. Multimedia Tools and Applications, Vol 82(14), pp. 21641–21655. DOI
  29. Wang, Y., Pang, W., Jiao, Z. (2023). An Adaptive Mutual K-nearest Neighbors Clustering Algorithm based on Maximizing Mutual Information. Pattern Recognition, Vol 137. DOI
  30. Gao, X., Taylor, S., Pang, W., Hui, R., Lu, X., Oxford GI investigators, Braden, B. (2023). Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time. Information Fusion, Vol 92, pp. 64–29. DOI
  31. Awad, A., Pang, W., Lusseau, D., Coghill, G. (2022). A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications. Artificial Intelligence Review, Vol 56, pp. 1–26. DOI AIR
  32. Naja, Y., Markovic, M., Edwards, P., Pang, W., Cottrill, C., Williams, R. (2022). Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information. IEEE Access, Vol 10. DOI
  33. Wang, Y., Pang, W., Zhou, J. (2022). An Improved Density Peak Clustering Algorithm Guided by Pseudo Labels. Knowledge-Based Systems, Vol 252, 109273. DOI KBS
  34. Liu, Q., Li, J., Ren, H., Pang, W. (2022). All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing. Knowledge-Based Systems, Vol 249, 108849. DOI KBS
  35. Hassan, M., Wang, Y., Pang, W., Wang, D., Li, D., Zhou, Y., Xu, D. (2022). IPAS-Net: A deep-learning model for high fidelity shoeprints from low-quality images with no natural references. Journal of King Saud University – Computer and Information Sciences, Vol 34, Issue 6, Part A, pp. 2743–2757. DOI
  36. Zhou, Y., Wang, Y., Wu, J., Hassan, M., Pang, W., Lv, L., Wang, L., Cui, H. (2022). ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deep learning. Multimedia Tools and Applications, Vol 81, pp. 25541–25556. DOI
  37. Hassan, M., Wang, Y., Wang, D., Pang, W., Wang, K., Li, D., Zhou, Y., Xu, D. (2022). Restorable-Inpainting: A Novel Deep Learning Approach for Shoeprint Restoration. Information Sciences, Vol 600, pp. 22–42. DOI
  38. J. Pathol.: Clin. Res.Alnabulsi, A., Wang, T., Pang, W., Ionescu, M., Craig, S., Humphries, M., McCombe, K., Tellez, M., Alnabulsi, A., Murray, G. (2022). Identification of a prognostic signature in colorectal cancer using combinatorial algorithms driven analysis. The Journal of Pathology: Clinical Research, Vol 8, Issue 3, pp. 245–256. DOI
  39. Williams, R., Cloete, R., Cobbe, J., Cotterill, C., Edwards, P., Markovic, M., Naja, I., Ryan, F., Singh, J., Pang, W. (2022). From Transparency to Accountability of Intelligent Systems–moving beyond aspirations. Data & Policy, Vol 4, E7. DOI
  40. Wang, X., Liu, X., Pang, W., Jiang, A. (2022). Multiscale Increment Entropy: An approach for quantifying the physiological complexity of biomedical time series. Information Sciences, Vol 586, pp. 279–293. DOI
  41. Gao, X., Taylor, S., Pang, W., Hui, R., Lu, X., Braden, B. (2021). Computational colour contrast-enhancement improves endoscopic visibility of oesophageal squamous dysplasia and detection in AI-based system. Gut, 70(Suppl 4), poster papers. DOI
  42. Wang, Y., Chen, J., Xie, X., Yang, S., Pang, W., Huang, L., Zhang, S., Zhao, S. (2021). Minimum Distribution Support Vector Clustering. Entropy, 23(11):1473. DOI
  43. Hassan, M., Wang, Y., Pang, W., Di, W., Li, D., Xu, D. (2021). GUV-Net for high fidelity shoeprint generation. Complex & Intelligent Systems, Vol 8, pp. 933–947. DOI CAIS
  44. Ji, J., Li, Z., He, F., Feng, G., Pang, W., Zhao, X. (2021). A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data. IEEE Access, Vol 9, pp. 24913–24924. DOI
  45. Zeng, Q., Ma, X., Cheng, B., Zhou, E., Pang, W. (2020). GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning. IEEE Access, Vol 8, pp. 172882–172891. DOI
  46. Yu, X., Pang, W., Xu, Q., Liang, M. (2020). Mammographic Image Classification with Deep Fusion Learning. Scientific Reports, Vol 10, No 14361. DOI
  47. Wang, Y., Yang, Y., Guo, J., Xie, X., Liang, S., Zhang, R., Pang, W., Huang, L. (2020). Cancer genotypes prediction and associations analysis from imaging phenotypes: A survey on radiogenomics. Biomarkers in Medicine, Vol 14, No 12. DOI
  48. Yang, W., Wang, D., Pang, W., Tan, A.H., Zhou, Y. (2020). Goods Consumed during Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution. IEEE Access, Vol 8, pp. 110336–110350. DOI
  49. Usman, M., Pang, W., Coghill, G.M. (2020). Inferring Structure and Parameters of Dynamic System Models using Swarm Intelligence. Memetic Computing, Vol 12, pp. 267–282. DOI
  50. Wang, Y., Wang, Y., Song, Y., Xie, X., Huang, L., Pang, W., Coghill, G.M. (2020). An Efficient v-minimum Absolute Deviation Distribution Regression Machine. IEEE Access, Vol 8. DOI
  51. Environ. Int.Liu, X., Wang, X., Zhou, L., Xia, J., Pang, W. (2020). Spatial Imputation for Air Pollutants Data Sets Via Low Rank Matrix Completion Algorithm. Environment International, Vol 139, 105713. DOI
  52. Wang, Y., Wang, D., Pang, W., Miao, C., Tan, A., Zhou, Y. (2020). A Systematic Density-based Clustering Method Using Anchor Points. Neurocomputing, Vol 400, pp. 352–370. DOI Neurocomputing
  53. Ji, J., Pang, W., Li, Z., He, F., Feng, G., Zhao, X. (2020). Clustering Mixed Numeric and Categorical Data with Cuckoo Search. IEEE Access, Vol 8. DOI
  54. Wang, Y., Wang, D., Zhang, X., Pang, W., Miao, C., Tan, A., Zhou, Y. (2020). McDPC: Multi-center Density Peak Clustering. Neural Computing and Applications, Vol 32, pp. 13465–13478. DOI
  55. Wang, Y., Zhou, Y., Pang, W., Liang, Y., Wang, S. (2020). Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures. Tehnički vjesnik – Technical Gazette, Vol 27, No 1. DOI
  56. Yu, X., Zhang, Z., Wu, L., Pang, W., Chen, H., Yu, Z., Li, B. (2020). Deep Ensemble Learning for Human Action Recognition in Still Images. Complexity, Vol 2020, Article 9428612. DOI
  57. Xue, Y., Tang, T., Pang, W., Liu, A.X. (2020). Self-adaptive Parameter and Strategy based Particle Swarm Optimization for Large-scale Feature Selection Problems with Multiple Classifiers. Applied Soft Computing, Vol 88, 106031. DOI
  58. Parmar, M.D., Pang, W., Hao, D., Jang, J., Liupu, W., Zhou, Y. (2019). FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy. IEEE Access, Vol 7, pp. 89789–89804. DOI
  59. Wang, X., Pang, W., Wang, Z. (2019). Meta Struct-CF: A Meta Structure Based Collaborative Filtering Algorithm in Heterogeneous Information Networks. Computer Science (in Chinese), Vol 46, No 6A, pp. 397–401. URL
  60. Wang, W., Moreau, N.G., Yuan, Y., Race, P.R., Pang, W. (2019). Towards machine learning approaches for predicting the self-healing efficiency of materials. Computational Materials Science, Vol 168, pp. 180–187. DOI
  61. Tian, X., Pang, W., Wang, Y., Guo, K., Zhou, Y. (2019). LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models. BioSystems, Vol 182, pp. 8–16. DOI
  62. Hu, X., Huang, L., Wang, Y., Pang, W. (2019). Explosion gravitation field algorithm with dust sampling for unconstrained optimization. Applied Soft Computing, Vol 81, 105500. DOI
  63. Yu, X., Yu, Z., Wu, L., Pang, W., Lin, C. (2019). Data-driven two-layer visual dictionary structure learning. Journal of Electronic Imaging, Vol 28, No 2, 023006. DOI
  64. Xue, Y., Jia, W., Zhao, X., Pang, W. (2018). An Evolutionary Computation Based Feature Selection Method for Intrusion Detection. Security and Communication Networks, Vol 2018, 2492956. DOI
  65. Wang, Y., Pang, W., Zhou, Y. (2018). Density propagation based adaptive multi-density clustering algorithm. PloS ONE, Vol 13, No 7, e0198948. DOI
  66. Yu, X., Yu, Z., Pang, W., Li, M., Wu, L. (2018). An improved EMD-based dissimilarity Metric for Unsupervised Linear Subspace Learning. Complexity, Vol 2018, 8917393. DOI
  67. Li, D., Huang, L., Wang, K., Pang, W., Zhou, Y., Zhang, R. (2018). A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs. IEEE Access, Vol 6, pp. 72327–72344. DOI
  68. Xue, Y., Jiang, J., Ma, T., Liu, J., Geng, H., Pang, W. (2018). A Self-adaptive Artificial Bee Colony Algorithm with Symmetry Initialization. Journal of Internet Technology, Vol 19, No 5, pp. 1347–1362. DOI
  69. Xue, Y., Zhao, B., Ma, T., Pang, W. (2018). A Self-adaptive Fireworks Algorithm for Classification Problems. IEEE Access, Vol 6, pp. 44406–44416. DOI
  70. Huang, L., Wang, G., Wang, Y., Pang, W., Ma, Q. (2016). A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection. International Journal of Modern Physics B, Vol 30, No 24, 1650167. DOI
  71. Wang, G., Huang, L., Wang, Y., Pang, W., Ma, Q. (2016). Link community detection based on line graphs with a novel link similarity measure. International Journal of Modern Physics B, Vol 30, No 6, 1650023. DOI
  72. Bone, J.D., Emele, C.D., Abdul, A.O., Coghill, G.M., Pang, W. (2016). The social sciences and the web: From 'Lurking' to interdisciplinary 'Big Data' research. Methodological Innovations, Vol 9, pp. 1–14. DOI
  73. Wu, Z., Pang, W., Coghill, G.M. (2015). An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing. Cognitive Computation, Vol 7, No 6, pp. 637–651. DOI
  74. Lin, C., Liu, D., Pang, W., Wang, Z. (2015). Sherlock: a Semi-Automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure. Cognitive Computation, Vol 7, No 6, pp. 667–679. DOI
  75. Du, W., Cao, Z., Wang, Y., Pang, W., Zhou, F., Tian, Y., Liang, Y. (2015). Specific biomarkers: detection of cancer biomarkers through high-throughput transcriptomics data. Cognitive Computation, Vol 7, No 6, pp. 652–666. DOI
  76. IJPRAIJi, J., Pang, W., Zheng, Y., Wang, Z., Ma, Z. (2015). An initialization method for clustering mixed numeric and categorical data based on the density and distance. International Journal of Pattern Recognition and Artificial Intelligence, Vol 29, No 7, 1550024. DOI
  77. Wu, Z., Pang, W., Coghill, G.M. (2015). An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems. Soft Computing, Vol 19, No 6, pp. 1595–1610. DOI
  78. Jiang, Y., Wang, Y., Pang, W., Chen, L., Sun, H., Liang, Y., Blanzieri, E. (2015). Essential Protein Identification Based on Essential Protein: Protein Interaction Prediction by Integrated Edge Weights. Methods, Vol 83, pp. 51–62. DOI
  79. Ji, J., Pang, W., Zheng, Y., Wang, Z., Ma, Z. (2015). A novel artificial bee colony based clustering algorithm for categorical data. PloS ONE, Vol 10, No 5, e0127125. DOI
  80. Pang, W., Coghill, G.M. (2015). Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast. BioSystems, Vol 131, pp. 40–50. DOI Code (JMorven)
  81. Pang, W., Coghill, G.M. (2015). QML-AiNet: an immune network approach to learning qualitative differential equation models. Applied Soft Computing, Vol 27, pp. 148–157. DOI
  82. Ji, J., Pang, W., Zheng, Y., Wang, Z., Ma, Z., Zhang, L. (2015). A novel cluster center initialization for the k-prototypes algorithms using centrality and distance. Applied Mathematics & Information Sciences, Vol 9, No 6, pp. 2933–2942. DOI
  83. Ma, D., Yu, J., Yu, Z., Pang, W. (2015). A novel object tracking algorithm based on compressed sensing and entropy of information. Mathematical Problems in Engineering, Vol 2015, 628101. DOI
  84. Pang, W., Coghill, G.M. (2014). QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches. Journal of Computational Science, Vol 5, No 5, pp. 795–808. DOI
  85. Li, B., Pang, W., Liu, Y., Yu, X., Du, A., Yu, Z. (2014). Dimension Reduction Using Samples' Inner Structure Based Graph for Face Recognition. Mathematical Problems in Engineering, Vol 2014, 603025. DOI
  86. Li, B., Pang, W., Liu, Y., Yu, X., Yu, Z. (2014). Building recognition on subregion's multi-scale gist feature extraction and corresponding columns information based dimensionality reduction. Journal of Applied Mathematics, Vol 2014, 898705. DOI
  87. Kaloriti, D., Tillmann, A., Cook, E., Jacobsen, M., You, T., Lenardon, M., Ames, L., Barahona, M., Chandrasekaran, K., Coghill, G., Goodman, D., Gow, N.A.R., Grebogi, C., Ho, H-L., Ingram, P., McDonagh, A., de Moura, A.P.S., Pang, W., et al. (2012). Combinatorial stresses kill pathogenic Candida species. Medical Mycology, Vol 50, No 7, pp. 699–709. DOI
  88. Ji, J., Pang, W., Han, X., Zhou, C., Wang, Z. (2012). A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data. Knowledge-Based Systems, Vol 30, pp. 129–135. DOI KBS
  89. Jia, C-C., Wang, S-J., Peng, X-J., Pang, W., Zhang, C-Y., Zhou, C., Yu, Z-Z. (2012). Incremental multi-linear discriminant analysis using canonical correlations for action recognition. Neurocomputing, Vol 83, pp. 56–63. DOI Neurocomputing
  90. Yu, Z-Z., Jia, C-C., Pang, W., Zhang, C-Y. (2012). Tensor Discriminant Analysis with Multi-Scale Features for Action Modeling and Categorization. IEEE Signal Processing Letters, Vol 19, No 2, pp. 95–98. DOI IEEE SPL
  91. Pang, W., Coghill, G.M. (2011). An immune-inspired approach to qualitative system identification of biological pathways. Natural Computing, Vol 10, No 1, pp. 189–207. DOI
  92. Liu, Y., Zhou, C., Guo, D., Wang, K., Pang, W., Zhai, Y. (2010). A decision support system using soft computing for modern international container transportation services. Applied Soft Computing, Vol 10, No 4, pp. 1087–1095. DOI
  93. Pang, W., Coghill, G.M. (2010). Learning Qualitative Differential Equation models: a survey of algorithms and applications. Knowledge Engineering Review, Vol 25, No 1, pp. 69–107. DOI KER
  94. Lv, C., Yu, Z., Zhou, C., Wang, K., Pang, W. (2005). A Dynamic and Adaptive Ant Algorithm Applied to Quadratic Assignment Problems. Journal of Jilin University (Science Edition), Vol 43, No 4, pp. 477–480.
  95. Pang, W., Wang, K., Zhou, C., Huang, L., Ji, X. (2005). Fuzzy Discrete Particle Swarm Optimization for Solving Travel Salesman Problem. Journal of Chinese Computer Systems, Vol 26, No 8, pp. 1331–1334.
  96. Huang, L., Pang, W., Wang, K., Zhou, C., Lv, Y. (2005). New Genetic Algorithm for Vehicle Routing Problem with Time Window. Journal of Chinese Computer Systems, Vol 26, No 2, pp. 214–217.
  97. Huang, L., Pang, W., Wang, K., Zhou, C., Xiao, Y. (2004). Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows. Advances in Systems Science and Applications, Vol 4, No 1, pp. 118–124.
  98. Huang, L., Wang, K., Zhou, C., Pang, W., Dong, L. (2003). Particle Swarm Optimization for Traveling Salesman Problems. Acta Scientiarium Naturalium Universitatis Jilinensis, Vol 41, No 4, pp. 477–480.
  99. Huang, L., Wang, K., Zhou, C., Yuan, Y., Pang, W. (2002). Hybrid Approach Based on Ant Algorithm for Solving Traveling Salesman Problem. Acta Scientiarium Naturalium Universitatis Jilinensis, Vol 40, No 4, pp. 369–373.

Conference Papers ()

  1. Guan, R., Wan, Y., Guo, C., Cao, B., Giunchiglia, F., Pang, W., Liu, Y., Feng, X. (2026). Advancing Graph Few-Shot Learning via In-Context Learning. 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Accepted. KDD 2026
  2. Han, X., Chen, K., Dai, X., Liang, D., Peng, M., Pang, W., Giunchiglia, F., Feng, X., Liu, Y., Guan, R. (2026). TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning. 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Accepted. KDD 2026
  3. Yang, Z., Xu, D., Zhang, Z., Chen, K., Wang, X., Xu, Y., Pang, W., Yuan, Y. (2026). Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models. ICML 2026, Accepted. ICML 2026
  4. Yang, Z., Yang, Z., Zhan, S., Yue, T., Pang, W., Yuan, Y. (2026). SVAgent: Storyline-guided Long Video Understanding via Cross-modal Multi-agent Collaboration. CVPR 2026, Accepted. arXiv CVPR 2026
  5. Mao, Y., Li, H., Pan, Y., Papanastasiou, G., Qi, P., Yang, Y., Pang, W., Wang, C. (2026). Data-Efficient Medical Segmentation Through Active Knowledge Distillation from a SAM Teacher. IEEE 23rd International Symposium on Biomedical Imaging (ISBI), Accepted.
  6. Yang, Z., Pang, W., Yuan, Y. (2026). XR: Cross-Modal Agents for Composed Image Retrieval. The ACM Web Conference 2026, Accepted. arXiv WWW 2026
  7. AAAI SPARTAJiang, Z., Yuan, Y., Hu, L., Pang, W. (2026). STProtein: predicting spatial protein expression from multi-omics data. AAAI 2026 Workshop SPARTA. OpenReview arXiv
  8. AAAI 2026Yang, Z., Yuan, Y., Jiang, X., An, B., Pang, W. (2026). InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration. AAAI 2026. arXiv DOI
  9. Zhang, Y., Han, B., Li, X., Pang, W., Giunchiglia, F., Feng, X., Guan, R. (2026). SPARD: Single-step Inference with Adaptive Sampling in Residual Diffusion for Human Motion Prediction. AAAI 2026. DOI AAAI 2026
  10. GECCO 2026Zhou, Y., Pang, W., Wang, W. (2026). Particle Swarm Optimization with Population Dynamics: Artificial Splitting, Extinction, and Migration. The Genetic and Evolutionary Computation Conference 2026 (GECCO 2026), San José, Costa Rica. URL
  11. Wang, T., Pang, W., Ma, X. (2025). Asymptotically Stable Quaternion-valued Hopfield-structured Neural Network with Periodic Projection-based Supervised Learning Rules. Thirty-Ninth Annual Conference on Neural Information Processing Systems. OpenReview arXiv NeurIPS 2025
  12. Liu, Y., Wang, Y., Guo, Y., Pang, W., Li, X., Giunchiglia, F., Feng, X., Guan, R. (2025). Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration. OpenReview arXiv NeurIPS 2025
  13. Yang, Z., Song, J., Luo, Z., Yang, Z., Xu, Y., Lan, J., Zhang, Y., Pang, W., Song, S., Yuan, Y. (2025). ReChar: Revitalising Characters with Structure Preserved and User-Specified Aesthetic Enhancements. SIGGRAPH Asia 2025. DOI
  14. Lihard, A., Pang, W. (2025). Stabilise Power Grid Systems from Fluctuating Renewable Energy Sources Production with Artificial Immune System. 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), Trondheim, Norway, pp. 1–5. DOI
  15. Yang, Z., Song, J., Song, S., Pang, W., Yuan, Y. (2025). MERMAID: Multi-perspective Self-reflective Agents with Generative Augmentation for Emotion Recognition. Empirical Methods in Natural Language Processing (EMNLP Main 2025). EMNLP
  16. ADMA 2025Wang, Y., Zhang, S., Yang, M., Zhang, T., Wang, J., Zhao, Y., Pang, W. (2025). IO-K-means: Iterative Optimization for Centroids in K-means. 21st International Conference on Advanced Data Mining and Applications. DOI
  17. Zheng, A., Pang, W., Wang, C. (2025). A Dual-Branch Super-Deep MambaPlusResGCN for Node Classification: Achieving Robustness Against Over-Smoothing. 24th UK Workshop on Computational Intelligence. DOI UKCI 2025
  18. Patel, R., Zhang, Y., Nicol, R., Chen, D., Pang, W. (2025). Artificial Immune System Approaches to Classify Ambiguous Data on Device Quality. 24th UK Workshop on Computational Intelligence. DOI UKCI 2025
  19. Li, B., Li, S., Pang, W. (2025). TS-Net: An Emotion Recognition Network Based on Temporal-Spatial Features of EEG Signals. International Conference on Intelligent Computing. DOI ICIC 2025
  20. Zhao, P., Cao, Z., Wang, D., Song, W., Pang, W., Zhou, Y., Jiang, Y. (2025). Visual-Enhanced Multimodal Framework for Flexible Job Shop Scheduling Problem. ACM Multimedia 2025. DOI ACM MM 2025
  21. Yuan, Y., Chen, K., Rizvi, M., Baillie, L., Pang, W. (2025). Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness. 2025 International Joint Conference on Neural Networks. Preprint DOI IJCNN 2025
  22. Liu, Y., Li, M., Pang, W., Giunchiglia, F., Huang, L., Feng, X., Guan, R. (2025). Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning. 39th Annual AAAI Conference on Artificial Intelligence. Preprint DOI AAAI 2025
  23. Wang, M., Zhou, Y., Cao, Z., Xiao, Y., Wu, X., Pang, W., Jiang, Y., Yang, H., Zhao, P. (2025). An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem. 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Preprint DOI KDD 2025
  24. GECCO 2024Song, J., Yuan, Y., Pang, W. (2024). SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm. GECCO 2024. Preprint DOI
  25. Yuan, Y., Wang, W., Li, X., Chen, K., Zhang, Y., Pang, W. (2024). Evolving Molecular Graph Neural Networks with Hierarchical Evaluation Strategy. GECCO 2024. DOI GECCO 2024
  26. Gavriilidis, K., Konstas, I., Hastie, H., Pang, W. (2024). Enhancing Situation Awareness through Model-Based Explanation Generation. Proceedings of the 2nd Workshop on Practical LLM-assisted Data-to-Text Generation. DOI PracticalD2T
  27. Yang, F., Li, X., Wang, M., Zang, H., Pang, W., Wang, M. (2023). WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series. AAAI Conference on Artificial Intelligence, 37(9), 10754–10761. DOI AAAI 2023
  28. ICRA WorkshopGavriilidis, K., Munafo, A., Pang, W., Hastie, H. (2023). A Surrogate Model Framework for Explainable Autonomous Behaviour. IEEE ICRA 2023 Workshop on Explainable Robotics. PDF arXiv
  29. ANNPR 2022Ghadage, A., Yi, D., Coghill, G., Pang, W. (2022). Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions. Artificial Neural Networks in Pattern Recognition (ANNPR 2022). Lecture Notes in Computer Science, Vol 13739. DOI
  30. Rajan, D., Jiang, S., Yi, D., Pang, W., Coghill, G. (2022). Enhanced Affinity Propagation Clustering on Heterogeneous Information Network. Advances in Computational Intelligence Systems. DOI UKCI 2022
  31. Gao, X., Taylor, S., Pang, W., Lu, X., Braden, B. (2022). Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. SPIE Medical Imaging 2022, San Diego, USA. URL DOI
  32. Korica, P., Gayar, N.E., Pang, W. (2021). Explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space. IDEAL 2021. Lecture Notes in Computer Science, Vol 13113. DOI
  33. Markovic, M., Naja, I., Edwards, P., Pang, W. (2021). The Accountability Fabric: A Suite of Semantic Tools For Managing AI System Accountability and Audit. 20th International Semantic Web Conference. PDF Demo ISWC 2021
  34. Gavriilidis, K., Carreno, Y., Munafo, A., Pang, W., Petrick, R., Hastie, H. (2021). Plan Verbalisation for Robots Acting in Dynamic Environments. ICAPS 2021 Workshop on Knowledge Engineering for Planning and Scheduling (KEPS). PDF Video KEPS 2021
  35. SICSA XAIForrest, J., Sripada, S., Coghill, G. (2021). Are Contrastive Explanations Useful? SICSA Workshop on eXplainable Artificial Intelligence 2021. PDF Video
  36. SICSA XAIZainyte, A., Pang, W. (2021). Challenges and Future Directions for Accountable Machine Learning. SICSA Workshop on eXplainable Artificial Intelligence 2021. PDF Video
  37. SICSA XAIPang, W., Markovic, M., Naja, I., Fung, C.P., Edwards, P. (2021). On Evidence Capture for Accountable AI Systems. SICSA Workshop on eXplainable Artificial Intelligence 2021. PDF Video
  38. SICSA XAIFung, C.P., Pang, W., Naja, I., Markovic, M., Edwards, P. (2021). Towards Accountability Driven Development for Machine Learning Systems. SICSA Workshop on eXplainable Artificial Intelligence 2021. PDF Video
  39. Yuan, Y., Wang, W., Pang, W. (2021). Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network for Molecular Property Prediction. GECCO '21 Companion, pp. 1403–1404. DOI arXiv Open Access GECCO 2021
  40. Yuan, Y., Wang, W., Pang, W. (2021). A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks. 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 482–489. DOI Open Access arXiv CEC 2021
  41. Frachon, L., Pang, W., Coghill, G. (2021). An Immune-Inspired Approach to Macro-Level Neural Ensemble Search. 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2491–2498. DOI arXiv CEC 2021
  42. Yuan, Y., Wang, W., Pang, W. (2021). A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction. GECCO '21, pp. 386–394. DOI arXiv GECCO 2021
  43. Rana, S., Ma, X., Wolverson, E., Pang, W. (2020). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. 7th IEEE/ACM BDCAT. DOI BDCAT 2020
  44. Wang, W., Pang, W., Bingham, P., Mania, M., Chen, T., Perry, J. (2020). Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels. IEEE Congress on Evolutionary Computation, Glasgow, pp. 1–7. DOI CEC 2020
  45. Rezvy, S., Zebin, T., Braden, B., Pang, W., Taylor, S., Gao, X. (2020). Transfer learning for endoscopy disease detection & segmentation with mask-RCNN benchmark architecture. EndoCV2020 workshop (ISBI 2020). PDF
  46. Gao, X., Braden, B., Zhang, L., Taylor, S., Pang, W., Pettdis, M. (2019). Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer. 25th UK Symposium on Case-Based Reasoning, Cambridge. PDF
  47. Gao, X., Braden, B., Taylor, S., Pang, W. (2019). Towards Real-Time Detection of Squamous Pre-cancers from Oesophageal Endoscopic Videos. 18th International Conference on Machine Learning and Applications (ICMLA), Boca Raton. DOI
  48. Xu, X., Gao, X., Xu, Z., Zhao, X., Pang, W., Zhou, H. (2019). TCPModel: A Short-Term Traffic Congestion Prediction Model Based on Deep Learning. 2nd CCF International Conference on Artificial Intelligence (CCF-ICAI 2019), Xuzhou. DOI
  49. Byla, E., Pang, W. (2019). DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence. 19th Annual UK Workshop on Computational Intelligence (UKCI 2019), Portsmouth. **Best Paper Award among 45 papers.** Code DOI UKCI 2019
  50. Albalawi, H., Pang, W., Coghill, G.M. (2019). Swarm Inspired Approaches for K-prototypes clustering. UKCI 2019, Portsmouth. DOI UKCI 2019
  51. Awad, A., Pang, W., Lusseau, D., Coghill, G.M. (2019). A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour. 2019 Conference on Artificial Life, Newcastle. DOI
  52. Awad, A., Usman, M., Lusseau, D., Coghill, G.M., Pang, W. (2019). A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems. GECCO '19 Companion, Prague. DOI GECCO 2019
  53. Usman, M., Awad, A., Pang, W., Coghill, G.M. (2019). Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. GECCO '19 Companion, Prague. DOI GECCO 2019
  54. Forrest, J., Sripada, S., Pang, W., Coghill, G. (2018). Towards making NLG a voice for interpretable Machine Learning. 11th International Conference on Natural Language Generation (INLG 2018), Tilburg. URL INLG 2018
  55. Awad, A., Pang, W., Coghill, G.M. (2018). Physarum Inspired Connectivity and Restoration for Wireless Sensor and Actor Networks. 18th UK Workshop on Computational Intelligence (UKCI 2018), Nottingham. DOI UKCI 2018
  56. SACI 2018Chapman, A., Pang, W., Coghill, G. (2018). CLEMI-imputation evaluation. IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI 2018), Timisoara. DOI
  57. Ou, G., Wang, Y., Huang, L., Pang, W., Coghill, G.M. (2018). ε-Distance Weighted Support Vector Regression. PAKDD 2018, Melbourne. DOI PAKDD 2018
  58. Chapman, A., Pang, W., Coghill, G. (2018). Towards a Robust Imputation Evaluation Framework. 7th International Conference on Intelligent Systems and Applications (INTELLI 2018), Venice. URL
  59. Karatu, M.T., Pang, W., Coghill, G.M. (2018). A Conceptual Framework of Starlings Swarm Intelligence Intrusion Prevention for Software Defined Networks. ReaLX'18: Reasoning, Learning & Explainability in AI, Aberdeen. PDF
  60. Pang, W., Bruce, A.M., Coghill, G.M. (2018). Non-constructive interval simulation of dynamic systems. 31st International Workshop on Qualitative Reasoning (QR 2018, co-located at IJCAI'18), Stockholm. PDF QR 2018
  61. Awad, A., Pang, W., Coghill, G. (2018). Physarum Inspired Model for Mobile Sensor Nodes Deployment in the Presence of Obstacles. International Conference on Emerging Technologies in Computing (iCETiC 2018), London. DOI
  62. Ma, M., Pang, W., Huang, L., Wang, Z. (2017). A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks. PAKDD 2017, Jeju. DOI PAKDD 2017
  63. Huang, L., Hu, X., Wang, Y., Zhang, F., Liu, Z., Pang, W. (2017). Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. 4th International Conference on Systems and Informatics (ICSAI 2017), Hangzhou, pp. 1328–1333. URL
  64. Ou, G., Wang, Y., Pang, W., Coghill, G.M. (2017). Large Margin Distribution Machine Recursive Feature Elimination. ICSAI 2017, Hangzhou, pp. 1427–1432. URL
  65. Mukhtar, N., Coghill, G.M., Pang, W. (2016). FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. GECCO '16 Companion. DOI GECCO 2016
  66. Han, L., Huang, L., Yang, X., Pang, W., Wang, K. (2016). A Novel Spatio-Temporal Data Storage and Index Method for ARM-Based Hadoop Server. ICCCS 2016, Nanjing. DOI
  67. Wang, Y., Du, W., Liang, Y., Chen, X., Zhang, C., Pang, W., Xu, Y. (2016). PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. ADMA 2016, Gold Coast. **Best Paper Runner Up Award.** DOI ADMA 2016
  68. Peng, Q., Wang, Y., Ou, G., Huang, L., Pang, W. (2016). Partitioning Clustering Based on Support Vector Ranking. ADMA 2016, Gold Coast. DOI ADMA 2016
  69. Emele, C.D., Spakov, V., Pang, W., Bone, J.D., Coghill, G.M. (2015). ADOVA: Anomaly Detection in Online and Virtual spAces. COOS@AAMAS 2015, Istanbul. PDF COOS@AAMAS 2015
  70. Jia, C., Pang, W., Fu, Y. (2015). Mode-Driven Volume Analysis Based on Correlation of Time Series. ECCV 2014 Workshops. DOI
  71. Lin, C., Liu, D., Pang, W., Apeh, E. (2015). Automatically Predicting Quiz Difficulty Level Using Similarity Measures. K-CAP 2015, New York. DOI K-CAP 2015
  72. Pang, W., Wang, K., Ge, O., Li, H., Wang, Y., Huang, L. (2015). Clonal Selection Algorithm for Solving Permutation Optimisation Problems: A Case Study of Travelling Salesman Problem. International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015), Atlantis Press, pp. 575–580.
  73. Luo, C., Pang, W., Wang, Z., Lin, C. (2014). Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. 14th IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, pp. 917–922. DOI Code ICDM 2014
  74. Luo, C., Pang, W., Wang, Z. (2014). Semi-supervised clustering on heterogeneous information networks. PAKDD 2014, Tainan. DOI PAKDD 2014
  75. Pang, W., Coghill, G.M. (2014). An immune network approach to learning qualitative models of biological pathways. 2014 IEEE Congress on Evolutionary Computation, pp. 1030–1037. DOI IEEE CEC 2014
  76. Jiang, Y., Wang, Y., Pang, W., Chen, L., Sun, H., Liang, Y., Blanzieri, E. (2014). Essential Protein Identification based on Essential Protein-Protein Interaction Prediction by Integrated Edge Weights. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014), Belfast, pp. 480–483. DOI BIBM 2014
  77. Pang, W., Coghill, G.M. (2014). Fuzzy qualitative simulation with multivariate constraints. 2014 IEEE International Conference on Fuzzy Systems, pp. 575–582. DOI FUZZ-IEEE 2014
  78. Pang, W., Coghill, G.M. (2013). An Immune Network Approach to Qualitative System Identification of Biological Pathways. 27th International Workshop on Qualitative Reasoning (QR 2013), Bremen, pp. 77–84. QR 2013
  79. Wu, Z., Pang, W., Coghill, G.M. (2013). Stepwise modelling of biochemical pathways based on qualitative model learning. 13th UK Workshop on Computational Intelligence (UKCI 2013), pp. 31–37. DOI UKCI 2013
  80. Pang, W., Coghill, G.M. (2012). Extended Kernel Subsets Analysis for Qualitative Model Learning. 12th UK Workshop on Computational Intelligence (UKCI 2012), Edinburgh, pp. 1–7. DOI UKCI 2012
  81. Pang, W., Coghill, G.M. (2011). A fast opt-AINet approach to qualitative model learning with a modified mutation operator. 11th UK Workshop on Computational Intelligence (UKCI), Manchester. UKCI 2011
  82. Pang, W., Coghill, G.M. (2010). Learning Qualitative Metabolic Models Using Evolutionary Methods. 5th International Conference on Frontier of Computer Science and Technology (FCST 2010), Changchun, pp. 436–441. DOI FCST 2010
  83. Pang, W., Coghill, G.M. (2010). QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. 8th International Conference on Artificial Immune Systems (ICARIS 2010). Lecture Notes in Computer Science, Vol 6209, pp. 223–236. DOI ICARIS 2010
  84. ICARIS 2009Pang, W., Coghill, G.M. (2009). An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal. Lecture Notes in Computer Science, Vol 5666, pp. 151–164. DOI
  85. Liu, Y., Wang, K., Guo, D., Pang, W., Zhou, C. (2008). Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem. 7th Mexican International Conference on Artificial Intelligence (MICAI '08), pp. 287–292. DOI MICAI 2008
  86. Pang, W., Coghill, G.M. (2008). Learning qualitative models of the detoxification pathway of methylglyoxal. 8th Annual UK Workshop on Computational Intelligence (UKCI 2008), De Montfort University. UKCI 2008
  87. Pang, W., Coghill, G.M. (2007). Advanced experiments for learning qualitative compartment models. 21st International Workshop on Qualitative Reasoning, Aberystwyth, pp. 109–117. PDF QR 2007
  88. Pang, W. (2007). Clonal selection algorithm for learning qualitative compartmental models of metabolic systems. 7th Annual UK Workshop on Computational Intelligence (UKCI 2007), London. UKCI 2007
  89. Pang, W., Coghill, G.M. (2007). Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems. GECCO 2007 Conference Companion, pp. 2887–2894. DOI GECCO 2007
  90. Meng, Y., Li, W., Wang, Y., Guo, W., Pang, W. (2006). An Evolution Computation Based Approach to Synthesize Video Texture. 6th International Conference on Computational Science (ICCS 2006), Reading. Lecture Notes in Computer Science, Vol 3992, pp. 223–230. DOI
  91. Pang, W., Coghill, G.M. (2006). EQML – An Evolutionary Qualitative Model Learning Framework. 2nd European Symposium on Nature-inspired Smart Information Systems, Tenerife, pp. 1–7. PDF
  92. Pang, W., Coghill, G.M. (2006). Evolutionary approaches for learning qualitative compartment metabolic models. 6th Annual UK Workshop on Computational Intelligence, Leeds, pp. 11–16.
  93. Pang, W., Wang, K., Zhou, C., Dong, L., Yin, Z. (2004). Fuzzy discrete particle swarm optimization for solving traveling salesman problem. 4th International Conference on Computer and Information Technology (CIT2004), Wuhan, pp. 796–800. DOI
  94. Pang, W., Wang, K., Zhou, C., Dong, L., Liu, M., Zhang, H., Wang, J. (2004). Modified particle swarm optimization based on space transformation for solving traveling salesman problem. 2004 International Conference on Machine Learning and Cybernetics, Vol 4, pp. 2342–2346. DOI
  95. Wang, K., Huang, L., Zhou, C., Pang, W. (2003). Particle swarm optimization for traveling salesman problem. 2003 International Conference on Machine Learning and Cybernetics, Vol 3, pp. 1583–1585. DOI

Book Chapters ()

  1. Book ChapJia, C., Pang, W., Fu, Y. (2016). Multimodal Action Recognition. In Y. Fu (ed.), Human Activity Recognition and Prediction. Springer, pp. 71–85. DOI
  2. Comput. MethodsLiu, M., Pang, W., Wang, K.P., Zhou, C.G. (2006). Improved Immune Genetic Algorithm For Solving Flow Shop Scheduling Problem. In G.R. Liu, V.B.C. Tan, X. Han (eds.), Computational Methods. Springer, pp. 1057–1062. DOI

Abstracts ()

  1. MycosesKaloriti, D., Tillmann, A., Jacobsen, M., Yin, Z., Patterson, M., Radmaneshfar, E., You, T., Chandrasekaran, K., Pang, W., Coghill, G., et al. (2012). Impact of combinatorial stresses upon Candida albicans. Mycoses, Vol 55, Suppl 4, p. 15. DOI

Technical Reports ()

  1. Tech ReportPang, W., Coghill, G.M., Bruce, A.M. (2012). Non-constructive interval simulation of dynamic systems. Technical Report ABDN–CS–12–02, Department of Computing Science, University of Aberdeen. URL
  2. Tech ReportPang, W., Coghill, G.M. (2012). QML-Morven: A Novel Framework for Learning Qualitative Models. Technical Report ABDN–CS–12–03, Department of Computing Science, University of Aberdeen. URL

ArXiv Papers ()

  1. arXivLiu, Y., Sun, J., Pang, W., Giunchiglia, F., Li, X., Feng, X., Guan, R. (2026). Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion. arXiv preprint arXiv:2604.27462. arXiv
  2. arXivWang, T., Chen, B., Zuo, Q., Xia, Q., Li, X., Pang, W. (2026). Planning Neural Dynamics with Lie Group Embedding through Supervised Projective Manifold Learning. arXiv preprint arXiv:2605.26167. arXiv
  3. arXivChen, K., Pang, W. (2020). ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures. arXiv Code
  4. arXivFrachon, L., Pang, W., Coghill, G. (2019). ImmuNeCS: Neural Committee Search by an Artificial Immune System. arXiv
  5. arXivByla, E., Pang, W. (2019). DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence. arXiv Code
  6. arXivWang, Y., Ou, G., Pang, W., Huang, L., Coghill, G.M. (2016). ε-Distance Weighted Support Vector Regression. arXiv
  7. arXivLuo, C., Pang, W., Wang, Z. (2014). Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. arXiv