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Selected Publications (Since 2021) 

Pre-Prints

  1. Haokai Hong, Wanyu Lin, Kay Chen Tan,“ Fast 3D Molecule Generation via Unified Geometric Optimal Transport”, arXiv preprint arXiv:2405.15252,2024.

  2. Xingyu Wu, Yan Zhong, Jibin Wu, Yuxiao Huang, Shenghao Wu, Kay Chen Tan,“ Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection”, arXiv preprint arXiv:2405.11349,2024.

  3. Zeyi Wang, Songbai Liu, Jianyong Chen, Kay Chen Tan,“ Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization”, arXiv preprint arXiv:2405.05767,2024.

  4. Zhenzhong Wang, Qingyuan Zeng, Wanyu Lin, Min Jiang, Kay Chen Tan,“ Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data”, arXiv preprint arXiv:2404.12569,2024.

  5. Yu Zhou, Xingyu Wu, Beicheng Huang, Jibin Wu, Liang Feng, Kay Chen Tan,“ CausalBench: A Comprehensive Benchmark for Causal Learning Capability of Large Language Models”, arXiv preprint arXiv:2404.06349,2024.

  6. Beichen Huang, Xingyu Wu, Yu Zhou, Jibin Wu, Liang Feng, Ran Cheng, Kay Chen Tan,“Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models”，arXiv preprint arXiv:2404.06290,2024.

  7. Haokai Hong, Wanyu Lin and Kay Chen Tan, “Diffusion-Driven Domain Adaptation for Generating 3D Molecules”, arXiv preprint arXiv:2404.00962, 2024.

  8. Yuxiao Huang, Wenjie Zhang, Liang Feng, Xingyu Wu and Kay Chen Tan - “How multimodal integration boost the performance of llm for optimization: Case study on capacitated vehicle routing problems”, arXiv preprint arXiv:2403.01757, 2024.

  9. Xingyu Wu, Shenghao Wu, Jibin Wu, Liang Feng and Kay Chen Tan – “Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap”, arXiv preprint arXiv:2401.10034, 2024.

  10. Xinmeng Xu, Jibin Wu, Xiaoyong Wei, Yan Liu, Richard So, Yuhong Yang, Weiping Tu and Kay Chen Tan , “SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping”, arXiv preprint arXiv:2311.01679, 2023.

  11. Wu, Xingyu, Yan Zhong, Jibin Wu, and Kay C. Tan. "AS-LLM: When Algorithm Selection Meets Large Language Model." , arXiv preprint arXiv:2310.12538.

  12. Qu Yang, Malu Zhang, Jibin Wu, Kay Chen Tan, Haizhou Li, “LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding”, arXiv preprint arXiv:2310.14978, 2023.

  13. Huan Zhang, Jinliang Ding, Liang Feng, Kay Chen Tan, Ke Li, “Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning”, arXiv preprint arXiv:2310.12538, 2023.

  14. Xiang Hao, Jibin Wu, Jianwei Yu, Chenglin Xu and Kay Chen Tan “Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction”, arXiv preprint arXiv:2310.07284, 2023.

  15. Xinyi Chen, Jibin Wu, Huajin Tang, Qinyuan Ren, Kay Chen Tan, “Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding” , arXiv preprint arXiv:2308.15150

  16. Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan, “Long Short-term Memory with Two-Compartment Spiking Neuron”, arXiv preprint arXiv:2307.07231.

2024

  1. Xiang Hao, Di Xu, Yang Zhao, Xin Meng, Jibin Wu, "Pink-Eggs Dataset: A Step Toward Invasive Species Management Using Deep Learning Solutions". The 11th IEEE International Conference on Cybernetics and Intelligent Systems & Robotics, Automation and Mechatronics (CIS-RAM 2024).

  2. Yinglan Feng, Liang Feng, Songbai Liu, Sam Kwong and Kay Chen Tan, “Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking”, Swarm and Evolutionary Computation, 2024​.

  3. Junchuang Cai, Qingling Zhu, Qiuzhen Lin, Zhong Ming and Kay Chen Tan, "Decomposition-Based Multiobjective Evolutionary Optimization With Tabu Search for Dynamic Pickup and Delivery Problems",  IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2024.3398781.

  4. Yulong Ye, Songbai Liu, Junwei Zhou, Qiuzhen Lin, Min Jiang and Kay Chen Tan, "Learning-Based Directional Improvement Prediction for Dynamic Multiobjective Optimization", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2024.3393151.

  5. Haokai Hong, Min Jiang, Qiuzhen Lin and Kay Chen Tan, "Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach",  IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2024.3386866.

  6. Rui Liu, Zhi -An Huang, Yao Hu, Lei Huang, Ka-Chun Wong and Kay Chen Tan, "Spatio-Temporal Hybrid Attentive Graph Network for Diagnosis of Mental Disorders on fMRI Time-Series Data",  IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2024.3386612.

  7. Jiaxin Chen, Jinliang Ding, Ke Li, Kay Chen Tan and Tianyou Chai, "A Knee Point Driven Evolutionary Algorithm for Multiobjective Bilevel Optimization",  IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2024.3377272.

  8. XingyuWu, Yan Zhong, Jibin Wu, Bingbing Jiang, Kay Chen Tan. "Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation". Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024.

  9. Xiang Hao, Chenxiang Ma, QuYang, Kay Chen Tan, and, Jibin Wu, "When Audio Denoising Meets Spiking Neural Network", Proceedings of 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, 2024.

  10. Wu Lin, Qiuzhen Lin, Xiaoming Xue and Kay Chen Tan, “Sequential Transfer via Clustering-Based Similarity Measurement for Faster Trajectory Optimization”, Proceedings of 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, 2024.

  11. Yajie Zhang, Zhi-an Huang, Jibin Wu and Kay Chen Tan, “Asymmetric Source-Free Unsupervised Domain Adaptation for Medical Image Diagnosis”, Proceedings of 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, 2024.

  12. Xiaoming Xue, Liang Feng, Cuie Yang, Songbai Liu, Linqi Song, and Kay Chen Tan, ``Multiobjective Sequential Transfer Optimization: Benchmark Problems and Results'', 2024 IEEE Congress on Evolutionary Computation (CEC),Yokohama, Japan, 2024.

  13. Yinglan Feng, Liang Feng, Xiaoming Xue, Sam Kwong, and Kay Chen Tan, "A Review on Evolutionary Multiform Transfer Optimization'', 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 2024.

  14. Xuan Duan, Songbai Liu, Junkai Ji, Lingjie Li, Qiuzhen Lin and Kay Chen Tan, "Evolutionary Multiobjective Feature Selection Assisted by Unselected Features'', 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 2024.

  15. Yao Hu,  Rui Liu, Jiaqi Zhang, Zhi-an Huang, Linqi Song, and Kay Chen Tan, "Heterogeneous Structured Federated Learning with Graph Convolutional Aggregation for MRI-Based Mental Disorder Diagnosis'', 2024 IEEE The International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024.

  16. Heping Liu, Songbai Liu, Qiuzhen Lin, Junkai Ji and Kay Chen Tan, "Personalized Federated Learning with Enhanced Implicit Generalization'', 2024 IEEE The International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024.

  17. Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu and Kay Chen Tan, ''Efficient Online Learning for Networks of Two-Compartment Spiking Neurons'', 2024 IEEE The International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024.

  18. Qiyuan Yu, Qiuzhen Lin, Junkai Ji, Wei Zhou, Shan He, Zexuan Zhu and Kay Chen Tan, "A Survey on Evolutionary Computation Based Drug Discovery", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2024.3382145.

  19. Zhenzhong Wang, Qingyuan Zeng, Wanyu Lin, Min Jiang, and Kay Chen Tan. 2024. “Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks”. Proceedings of the AAAI Conference on Artificial Intelligence 38 (19):21690-98. DOI:https://doi.org/10.1609/aaai.v38i19.30168.

  20. Songbai Liu, Zeyi Wang, Qiuzhen Lin, Jianqiang Li and Kay Chen Tan, "Learning-Aided Evolutionary Search and Selection for Scaling-up Constrained Multiobjective Optimization", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2024.3380366.

  21. Songbai Liu, Jun Li, Qiuzhen Lin, Ye Tian, Jianqiang Li and Kay Chen Tan, "Evolutionary Large-Scale Multiobjective Optimization via Autoencoder-Based Problem Transformation", IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2024.3369629.

  22. Gengzhi Zhang, Liang Feng, Yu Wang, Min Li, Hong Xie and Kay Chen Tan, "Reinforcement Learning With Adaptive Policy Gradient Transfer Across Heterogeneous Problems", IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2024.3361860.

  23. Jianping Luo, Yongfei Dong, Qiqi Liu, Zexuan Zhu, Wenming Cao, Kay Chen Tan and Yaochu Jin, "A New Multitask Joint Learning Framework for Expensive Multi-Objective Optimization Problems",  IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 2, pp. 1894-1909, April 2024, DOI: 10.1109/TETCI.2024.3359042.

  24. Yi Jiang, Zhi-Hui. Zhan, Kay Chen Tan, Sam Kwong and Jun Zhang, "Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2024.3355781.

  25. Zhenzhong Wang, Lulu Cao, Liang Feng, Min Jiang and Kay Chen Tan, "Evolutionary Multitask Optimization With Lower Confidence Bound-Based Solution Selection Strategy", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3349250.

  26. Linqiang Pan, Jianqing Lin, Handing Wang, Cheng He, Kay Chen Tan and Yaochu Jin, "Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition",  IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2024.3380327.

  27. Lulu Cao, Yufei Liu, Zhenzhong Wang, Dejun Xu, Kai Ye, Kay Chen Tan, and Min Jiang. 2024. “An Interpretable Approach to the Solutions of High-Dimensional Partial Differential Equations”. Proceedings of the AAAI Conference on Artificial Intelligence 38 (18):20640-48. DOI: https://doi.org/10.1609/aaai.v38i18.30050.

2023

  1. Xinyi Chen, Qu Yang, Jibin Wu, Haizhou Li, Kay Chen Tan, "A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2023.3339211.

  2. Xun Zhou, Zhenkun Wang, Liang Feng, Songbai Liu, Ka-Chun. Wong and Kay Chen Tan, "Towards Evolutionary Multi-Task Convolutional Neural Architecture Search",  IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2023.3348475.

  3. Junjia Liu, Hhengyi Sim, Chenzui Li, Kay Chen Tan and Fei Chen, "BiRP: Learning Robot Generalized Bimanual Coordination Using Relative Parameterization Method on Human Demonstration",  2023 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 8300-8305, DOI: 10.1109/CDC49753.2023.10383296.

  4. Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang, Linqi Song and Kay Chen Tan, "Solution Transfer in Evolutionary Optimization: An Empirical Study on Sequential Transfer", IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2023.3339506.​

  5. Yinglan Feng, Liang Feng, Sam Kwong and Kay Chen Tan, "A Multi-Form Evolutionary Search Paradigm for Bi-level Multi-Objective Optimization", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3332676.

  6. Qiuzhen Lin, Yulong Ye, Lijia Ma, Min Jiang and Kay Chen Tan, "Dynamic Multiobjective Evolutionary Optimization via Knowledge Transfer and Maintenance",  IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2023.3322718.

  7. Yuxiao Huang, Wei Zhou, Yu Wang, M. Li, L. Feng and K. C. Tan, "Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multi-Objective Optimization", IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3323877.

  8. Yao Hu, Zhi-an Huang,Rui Liu, Xiaoming Xue, Xiaoyan Sun, Linqin Song and Kay Chen Tan, "Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13778-13795, 1 Nov. 2023, DOI: 10.1109/TPAMI.2023.3298332.

  9. Kailai Gao, Cuie Yang, Jinliang Ding, Kay Chen Tan and Tianyou Chai, “Distributed Knowledge Transfer for Evolutionary Multitask Multimodal Optimization”, IEEE Transactions on Evolutionary Computation, in press, 2023. DOI: 10.1109/TEVC.2023.3291874.

  10. Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan and Jun Zhang, “Knowledge Learning for Evolutionary Computation”, IEEE Transactions on Evolutionary Computation, in press, 2023. DOI: 10.1109/TEVC.2023.3278132.

  11. Xiangning Xie, Yanan Sun, Yuqiao Liu, Mengjie Zhang and Kay Chen Tan, “Architecture Augmentation for Performance Predictor via Graph Isomorphism”, IEEE Transactions on Cybernetics, in press, 2023. DOI: 10.1109/TCYB.2023.3267109.

  12. Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang, “Block-Level Knowledge Transfer for Evolutionary Multitask Optimization”, IEEE Transactions on Cybernetics, in press, 2023. DOI: 10.1109/TCYB.2023.3273625.

  13. Yuxiao Huang, Liang Feng, Min Li, Yu Wang, Zexuan Zhu and Kay Chen Tan, "Fast Vehicle Routing via Knowledge Transfer in a Reproducing Kernel Hilbert Space", IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 9, pp. 5404-5416, Sept. 2023, doi: 10.1109/TSMC.2023.3270308.

  14. Wu Lin, Qiuzhen Lin, Liang Feng and Kay Chen Tan, “Ensemble of Domain Adaptation-Based Knowledge Transfer for Evolutionary Multitasking”, IEEE Transactions on Evolutionary Computation, in press, 2023. DOI: 10.1109/TEVC.2023.3259067.

  15. Lingjie Li, Manlin Xuan, Qiuzhen Lin, Min Jiang, Zhong Ming and Kay Chen Tan, “An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection”, IEEE Transactions on Evolutionary Computation, in press, 2023. DOI: 10.1109/TEVC.2023.3254155.

  16. Rui Liu, Zhi-an Huang, Yao Hu, Zhexuan Zhu, Ka-Chun Wong, and Kay Chen Tan, “Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders from MRI data”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2023. DOI: 10.1109/TNNLS.2023.3243000.

  17. Sheng-Hao Wu, Zhi-Hui Zhan, Kay Chen Tan and Jun Zhang, “Transferable Adaptive Differential Evolution for Many-Task Optimization”, IEEE Transactions on Cybernetics, in press, 2023. DOI: 10.1109/TCYB.2023.3234969.

  18. Junkai Ji, Minhui Dong, Qiuzhen Lin, and Kay Chen Tan, “Non-invasive Cuff-less Blood Pressure Estimation with Dendritic Neural Regression”, IEEE Transactions on Cybernetics, vol. 53, no. 7, pp. 4162-4174, Jul. 2023.

  19. Kangjia Qiao, Kunjie Yu, Boyang Qu, Jing Liang, Hui Song, Caitong Yue, Hongyu Lin and Kay Chen Tan, “Dynamic Auxiliary Task-Based Evolutionary Multitasking for Constrained Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 642-656, Jun. 2023.

  20. Jian-Yu Li, Zhi-Hui Zhan, Kay Chen Tan and Jun Zhang, “Dual Differential Grouping: A More General Decomposition Method for Large-Scale Optimization”, IEEE Transactions on Cybernetics, vol. 53, no. 6, pp. 3624-3638, Jun. 2023.

  21. Yao Hu, Xiaoyan Sun, Ye Tian, Linqi Song, Kay Chen Tan, “Communication Efficient Federated Learning With Heterogeneous Structured Client Models”, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 3, pp. 753-767, Jun. 2023.

  22. Songbai Liu, Qiuzhen Lin, Ka-Chun Wong, Qing Li, Kay Chen Tan, “Evolutionary Large-Scale Multiobjective Optimization: Benchmarks and Algorithms”, IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 401-415, Jun. 2023.

  23. Weizhong Wang, Hailin Liu and Kay Chen Tan, “A Surrogate-Assisted Differential Evolution Algorithm for High-Dimensional Expensive Optimization Problems”, IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2685-2697, Apr. 2023.

  24. Jing Liang, Xuanxuan Ban, Kunjie Yu, Boyang Qu, Kangjia Qiao, Caitong Yue, Ke Chen and Kay Chen Tan, “A Survey on Evolutionary Constrained Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 27, no. 2, pp. 201-221, Apr. 2023.

  25. Hongyan Chen, Hai-Lin Liu, Fangqing Gu and Kay Chen Tan, “A Multi-objective Multitask Optimization Algorithm Using Transfer Rank”, IEEE Transactions on Evolutionary Computation, vol. 27, no, 2, pp. 237-250, Apr. 2023.

  26. Zhi-An Huang, Yao Hu, Rui Liu, Xiaoming Xue, Zexuan Zhu, Linqi Song and Kay Chen Tan, “Federated Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders on MRI Scans”, IEEE Transactions on Biomedical Engineering, vol. 70, no. 4, pp. 1137- 1149, Apr. 2023.

  27. Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, and Jun Zhang, “Optimizing Niche Center for Multimodal Optimization Problems”, IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2544-2557, Apr. 2023.

  28. Sheng-Hao Wu, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang, “Orthogonal Transfer for Multitask Optimization”, IEEE Transactions on Evolutionary Computation, vol. 27, no.1, pp. 185-200, Feb. 2023.

  29. Songbai Liu, Jun Li, Qiuzhen Lin, Ye Tian and Kay Chen Tan, “Learning to Accelerate Evolutionary Search for Large-Scale Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 27, no. 1, pp. 64-81, Feb. 2023.

  30. Yuqiao Liu, Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen, Kay Chen Tan, “A Survey on Evolutionary Neural Architecture Search”, IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 550-570, Feb. 2023.

  31. Ye Tian, Langchun Si, Xingyi Zhang, Kay Chen Tan and Yaochu Jin, “Local Model Based Pareto Front Estimation for Multi-Objective Optimization”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 1, pp. 623-634, Jan. 2023.

  32. Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan, “A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks”, IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 1, pp. 446-460, Jan. 2023.

  33. Zhenzhong Wang, Lulu Cao, Wanyu Lin, Min Jiang, and Kay Chen Tan, “Robust Graph Meta-Learning via Manifold Calibration with Proxy Subgraphs”, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Washington DC, USA, Feb. 7–14, pp. 15224-15232, 2023.

  34. Xun Zhou, Songbai Liu, Ka-Chun Wong, Qiuzhen Lin, and Kay Chen Tan, “A Hybrid Search Method for Accelerating Convolutional Neural Architecture Searching”, 15th International Conference on Machine Learning and Computing (ICMLC), Zhu Hai, China, Feb. 17-20, pp. 1-10, 2023.

2022

  1. Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan and Kalyanmoy Deb, “Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment”, IEEE Transactions on Evolutionary Computation, in press, 2022. DOI: 10.1109/TEVC.2022.3233364.

  2. Yue Wu, Hangqi Ding, Maoguo Gong, A. K. Qin, Wenping Ma, Qiguang Miao and Kay Chen Tan, “Evolutionary Multiform Optimization with Two-stage Bidirectional Knowledge Transfer Strategy for Point Cloud Registration”, IEEE Transactions on Evolutionary Computation, in press, 2022. DOI: 10.1109/TEVC.2022.3215743.

  3. Cuie Yang, Bing Xue, Kay Chen Tan, Mengjie Zhang, “A Co-training Framework for Heterogeneous Heuristic Domain Adaptation”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. DOI: 10.1109/TNNLS.2022.3212924.

  4. Cheng He, Ran Cheng, Lianghao Li, Kay Chen Tan and Yaochu Jin, “Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis”, IEEE Transactions on Evolutionary Computation, in press, 2022. DOI: 10.1109/TEVC.2022.3213006.

  5. Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan and Jun Zhang, “A Bi-objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization”, IEEE Transactions on Evolutionary Computation, in press, 2022. DOI: 10.1109/TEVC.2022.3210783.

  6. Jia Zhang, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang, Jinyi, Long, Jian Weng and Kay Chen Tan, “Fast Multi-label Feature Selection via Global Relevance and Redundancy Optimization”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. DOI: 10.1109/TNNLS.2022.3208956.

  7. Xunfeng Wu, Qiuzhen Lin, Jianqiang Li, Kay Chen Tan and Victor C. M. Leung, “An Ensemble Surrogate-based Coevolutionary Algorithm for Solving Large-scale Expensive Optimization Problems”, IEEE Transactions on Cybernetics, in press, 2022. DOI: 10.1109/TCYB.2022.3200517.

  8. Fangfang Zhang, Yi Mei, Su Nguyen, Kay Chen Tan and Mengjie Zhang, “Task Relatedness-Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling”, IEEE Transactions on Evolutionary Computation, in press, 2022. DOI: 10.1109/TEVC.2022.3199783.

  9. Fangfang Zhang, Yi Mei, Su Nguyen, Kay Chen Tan and Mengjie Zhang, “Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling”, IEEE Transactions on Evolutionary Computation, in press, 2022. DOI: 10.1109/TEVC.2022.3180693.

  10. Yuchao Su, Qiuzhen Lin, Zhong Ming and Kay Chen Tan, “Adapting Decomposed Directions for Evolutionary Multiobjective Optimization”, IEEE Transactions on Cybernetics, in press, 2022. DOI: 10.1109/TCYB.2022.3165557.

  11. Junkai Ji, Jiajun Zhao, Qiuzhen Lin and Kay Chen Tan, “Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model”, IEEE Transactions on Cybernetics, in press, 2022. DOI: 10.1109/TCYB.2022.3165374.

  12. Zhi-an Huang, Rui Liu, Zhexuan Zhu, and Kay Chen Tan, “Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. DOI: 10.1109/TNNLS.2022.3225179.

  13. Rui Liu, Zhi-an Huang, Yao Hu, Zhexuan Zhu, Ka-Chun Wong, and Kay Chen Tan, “Attention-like Multimodality Fusion with Data Augmentation for Diagnosis of Mental Disorders using fMRI”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. DOI: 10.1109/TNNLS.2022.3219551.

  14. Hui Bai, Ran Cheng, Danial Yazdani, Kay Chen Tan and Yaochu Jin, “Evolutionary Large-Scale Dynamic Optimization Using Bi-level Variable Grouping”, IEEE Transactions on Cybernetics, in press, 2022. DOI: 10.1109/TCYB.2022.3164143.

  15. Junwei Dong, Boyu Hou, Liang Feng, Huajin Tang, Kay Chen Tan and Yew-Soon Ong, “A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. DOI: 10.1109/TNNLS.2022.3155230.

  16. Ye Tian, Xiaopeng Li, Haiping Ma, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin, “Deep Reinforcement Learning Based Adaptive Operator Selection for Evolutionary Multi-Objective Optimization”, IEEE Transactions on Emerging Topics in Computational Intelligence, in press, 2022. DOI: 10.1109/TETCI.2022.3146882.

  17. Ye Tian, Yuandong Feng, Chao Wang, Ruifen Cao, Xingyi Zhang, Xi Pei, Kay Chen Tan, and Yaochu Jin, “A Large-Scale Combinatorial Many-Objective Evolutionary Algorithm for Intensity-Modulated Radiotherapy Planning”, IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1511-1525, Dec. 2022.

  18. Songbai Liu, Qiuzhen Lin, Ye Tian, Kay Chen Tan, “A Variable Importance Based Differential Evolution for Large Scale Multiobjective Optimization”, IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 13048-13062, Dec. 2022.

  19. Si-Chen Liu, Zhi-Hui Zhan, Kay Chen Tan, and Jun Zhang, “A Multi-objective Framework for Many-objective Optimization”, IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 13654-13668, Dec. 2022.

  20. Xiaoming Xue, Cuie Yang, Yao Hu, Kai Zhang, Yiu-ming Cheung, Linqi Song, and Kay Chen Tan, “Evolutionary Sequential Transfer Optimization for Objective-Heterogeneous Problems”, IEEE Transactions on Evolutionary Computation, vol. 26, no.6, pp. 1424-1438, Dec. 2022.

  21. Jibin Wu, Chenglin Xu, Xiao Han, Daquan Zhou, Malu Zhang, Haizhou Li, Kay Chen Tan, “Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7824-7840, Nov. 2022.

  22. Jiaxin Li, Dengju Li, Runhao Jiang, Rong Xiao, Huajin Tang, and Kay Chen Tan, “Vision-Action Semantic Associative Learning Based on Spiking Neural Networks for Cognitive Robot”, IEEE Computational Intelligence Magazine, vol. 17, no. 4, pp. 27 – 38, Nov. 2022.

  23. Ye Tian, Haowen Chen, Haiping Ma, Xingyi Zhang, Kay Chen Tan and Yaochu Jin, “Integrating Conjugate Gradients into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization”, IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 10, pp. 1801-1817, Oct. 2022.

  24. Fangfang Zhang, Yi Mei, Su Nguyen, Kay Chen Tan, and Mengjie Zhang, “Multitasking Genetic Programming Based Generative Hyper-heuristics: A Case Study in Dynamic Scheduling", IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 10515-10528, Oct. 2022.

  25. Huan Zhang, Jinliang Ding, Min Jiang, Kay Chen Tan, and Tianyou Chai, “Inverse Gaussian Process Modeling for Evolutionary Dynamic Multiobjective Optimization”, IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 11240-11253, Oct. 2022.

  26. Jianqiang Li, Tao Sun, Qiuzhen Lin, Min Jiang, and Kay Chen Tan, “Reducing Negative Transfer Learning via Clustering for Dynamic Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 26, no.5, pp. 1102-1116, Oct. 2022.

  27. Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, and Yong Liu, “Evolutionary Search with Multi-View Prediction for Dynamic Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 26, no. 5, pp. 911-925, Oct. 2022.

  28. Songbai Liu, Qiuzhen Lin, Qing Li, and Kay Chen Tan, “A Comprehensive Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization”, IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 52, no. 9, pp. 5829-5842, Sep. 2022.

  29. Shangshang Yang, Ye Tian, Cheng He, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin, “A Gradient Guided Evolutionary Approach to Training Deep Neural Networks”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 4861-4875, Sep. 2022.

  30. Ye Tian, Yajie Zhang, Yansen Su, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin “Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multi-Objective Optimization”, IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9559-9572, Sep. 2022.

  31. Ye Tian, Shichen Peng, Shangshang Yang, Xingyi Zhang, Kay Chen Tan, Yaochu Jin, “Action Command Encoding for Surrogate Assisted Neural Architecture Search”, IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 3, pp. 1129-1142, Sep. 2022.

  32. Yansen Su, Zhongxiang Jin, Ye Tian, Xingyi Zhang, Kay Chen Tan, “Comparing the Performance of Evolutionary Algorithms for Sparse Multi-Objective Optimization via a Comprehensive Indicator”, IEEE Computational Intelligence Magazine, vol. 17, no. 3, pp. 34-53, Aug. 2022.

  33. Qiang Yu, Shiming Song, Chenxiang Ma, Jianguo Wei, Shengyong Chen, and Kay Chen Tan, “Temporal Encoding and Multi-spike Learning Framework for Efficient Recognition of Visual Patterns”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, pp. 3387-3399, Aug. 2022.

  34. Heba El-Fiqi, Min Wang, Kathryn Kasmarik, Anastasios Bezerianos, Kay Chen Tan, and Hussein A. Abbass, “Weighted Gate Layer Autoencoders”, IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7242-7253, Aug. 2022.

  35. Cuie Yang, Yiu-ming Cheung, Jinliang Ding, and Kay Chen Tan, “Concept Drift-tolerant Transfer Learning in Dynamic Environments”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, pp. 3857-3871, Aug. 2022.

  36. Jian-Yu Li, Zhi-Hui Zhan, Kay Chen Tan, and Jun Zhang, “A Meta-Knowledge Transfer-based Differential Evolution for Multitask Optimization”, IEEE Transactions on Evolutionary Computation, vol. 26, no. 4, pp. 719-734, Aug. 2022.

  37. Qiuzhen Lin, Zhixiong Fang, Yi Chen, Kay Chen Tan, and Yun Li, “Evolutionary Architectural Search for Generative Adversarial Networks”, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no.4, pp. 783-794, Aug. 2022.

  38. Zedong Tang, Maoguo Gong, Yue Wu, A. K. Qin, and Kay Chen Tan, “A Multifactorial Optimization Framework Based on Adaptive Inter-Task Coordinate System”, IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6745-6758, Jul. 2022.

  39. Xiaoming Xue, Kai Zhang, Kay Chen Tan, Liang Feng, Jian Wang, Guodong Chen, Xinggang Zhao, Liming Zhang, and Jun Yao, “Affine Transformation Enhanced Multifactorial Optimization for Heterogeneous Problems”, IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6217-6231, Jul. 2022.

  40. Yuxiao Huang, Liang Feng, A. K. Qin, Meng Chen, Kay Chen Tan, “Toward Large-Scale Evolutionary Multitasking: A GPU-Based Paradigm”, IEEE Transactions on Evolutionary Computation, vol. 26, no. 3, pp. 585-598, Jun. 2022.

  41. Liang Feng, Wei Zhou, Weichen Liu, Yew-Soon Ong, and Kay Chen Tan, “Solving Dynamic Multi-objective Problem via Autoencoding Evolutionary Search”, IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 2649-2662, May 2022.

  42. Jia Zhang, Shaozi Li, Min Jiang, and Kay Chen Tan, “Learning from Weakly Labeled Data Based on Manifold Regularized Sparse Model”, IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3841-3854, May 2022.

  43. Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello, “A Fuzzy Decomposition Based Multi/Many-objective Evolutionary Algorithm”, IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3495-3509, May 2022.

  44. Yinglan Feng, Liang Feng, Sam Kwong, and Kay Chen Tan, “A Multi-Variation Multifactorial Evolutionary Algorithm for Large-Scale Multi-Objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 248-262, Apr. 2022.

  45. Qiang Yu, Chenxiang Ma, Shiming Song, Gaoyan Zhang, Jianwu Dang, Kay Chen Tan, “Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1714-1726, Apr. 2022.

  46. Chaoda Peng, Hai-Lin Liu, Erik D. Goodman, Kay Chen Tan, “A two-phase framework of locating the reference point for decomposition-based constrained multi-objective evolutionary algorithms”, Knowledge-Based Systems, vol. 239, pp. 107933, Mar. 2022.

  47. Qiang Yu, Shiming Song, Chenxiang Ma, Linqiang Pan, and Kay Chen Tan, “Synaptic Learning with Augmented Spikes”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 3, pp. 1134-1146, Mar. 2022.

  48. Qiang Yu, Shenglan Li, Huajin Tang, Longbiao Wang, Jianwu Dang, Kay Chen Tan, “Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning”, IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1364-1376, Mar. 2022.

  49. Liang Feng, Yuxiao Huang, Ivor Tsang, Abhishek Gupta, Ke Tang, Kay Chen Tan, and Yew-Soon Ong, “Towards Faster Vehicle Routing by Transferring Knowledge from Customer Representation”, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 952-965, Feb. 2022.

  50. Lei Chen, Hai-Lin Liu, Kay Chen Tan, and Ke Li, “Transfer Learning Based Parallel Evolutionary Algorithm Framework for Bi-level Optimization”, IEEE Transactions on Evolutionary Computation, vol. 26, no. 1, pp. 115-129, Feb. 2022.

  51. Zhi-Hui Zhan, Lin Shi, Kay Chen Tan and Jun Zhang, “A Survey on Evolutionary Computation for Complex Continuous Optimization”, Artificial Intelligence Review, vol. 55, pp. 59–110, Jan. 2022.

  52. Haokai Hong, Kai Ye, Min Jiang, Donglin Cao and Kay Chen Tan, “Solving large-scale multiobjective optimization via the probabilistic prediction model”, Memetic Computing, vol. 14, no. 2, pp. 165-177, Jan. 2022.

  53. W. Shu, J. Wan, K. C. Tan, S. T. W. Kwong, and A. B. Chan, “Crowd Counting in the Frequency Domain”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, Jun. 19-24, pp. 19618-19627, 2022.

  54. Y. Hu, Z. Huang, R. Liu, X. Xue, L. Song, and K. C. Tan, “A Dual-Stage Pseudo-Labeling Method for the Diagnosis of Mental Disorder on MRI Scans”, International Joint Conference on Neural Network (IJCNN), Padua, Italy, Jul. 18-23, pp. 1-8, 2022.

  55. H. Hong, M. Jiang, L. Feng, Q. Lin and K. C. Tan, "Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism", IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, Jul. 18-23, pp. 1-8, 2022.

  56. S. Liu, Q. Lin, and K. C. Tan, "Evolutionary Large-Scale Multiobjective Optimization via Self-guided Problem Transformation", IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, Jul. 18-23, pp. 1-8, 2022.

2021

  1. Zhenzhong Wang, Haokai Hong, Kai Ye, Guang-en Zhang, Min Jiang, Kay Chen Tan, “Manifold Interpolation for Large-Scale Multiobjective Optimization via Generative Adversarial Networks”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2021, DOI: 10.1109/TNNLS.2021.3113158.

  2. Guodong Du, Jia Zhang, Min Jiang, Jinyi Long, Yaojin Lin, Shaozi Li, and Kay Chen Tan, “Graph-based Class-imbalance Learning with Label Enhancement”, IEEE Transactions on Neural Networks and Learning Systems, in press, 2021. DOI: 10.1109/TNNLS.2021.3133262.

  3. Ye Tian, Lang Chun Si, Xingyi Zhang, Ran Cheng, Cheng He, Kay Chen Tan, Yaochu Jin, “Evolutionary Large-Scale Multi-Objective Optimization: A Survey”, ACM Computing Surveys, vol. 54, no. 8, pp. 1-34, Oct. 2021.

  4. Min Jiang, Zhenzhong Wang, Shihui Guo, Xing Gao, and Kay Chen Tan, “Individual-based Transfer Learning for Dynamic Multiobjective Optimization”, IEEE Transactions on Cybernetics, vol. 51, no. 10, pp. 4968-4981, Oct. 2021.

  5. Jibin Wu, Qi Liu, Malu Zhang, Zihan Pan, Haizhou Li, Kay Chen Tan, “HuRAI: A Brain-Inspired Computational Model for Human-Robot Auditory Interface”, Neurocomputing, vol. 465, pp. 103-113, Sep. 2021.

  6. Zhi-An Huang, Jia Zhang, Zexuan Zhu, Edmond Q. Wu, and Kay Chen Tan, “Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multi-Label Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 3971-3984, Sep. 2021.

  7. Xinye Cai, Wenxue Sun, Mustafa Misir, Kay Chen Tan, Xiaoping Li, Tao Xu, Zhun Fan, “A Bi-Objective Constrained Robust Gate Assignment Problem: Formulation, Instances and Algorithm”, IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4488-4500, Sep. 2021.

  8. Junkai Ji, Minhui Dong, Qiuzhen Lin, Kay Chen Tan, “Forecasting Wind Speed Time Series Via Dendritic Neural Regression”, IEEE Computational Intelligence Magazine, vol. 16, no. 3, pp. 50-66, Aug. 2021.

  9. Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang, and Kay Chen Tan, “Surrogate-Assisted Evolutionary Multitasking Genetic Programming for Dynamic Flexible Job Shop Scheduling”, IEEE Transactions on Evolutionary Computation, vol. 25, no. 4, pp. 651-665, Aug. 2021.

  10. Zhi-An Huang, Zexuan Zhu, Chuen Heung Yau, and Kay Chen Tan, “Identifying Autism Spectrum Disorder from Resting-State fMRI Using Deep Belief Network”, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2847-2861, Jul. 2021.

  11. Min Jiang, Zhenzhong Wang, Liming Qiu, Shihui Guo, Xing Gao, Kay Chen Tan, “A Fast Dynamic Evolutionary Multi-objective Algorithm via Manifold Transfer Learning”, IEEE Transactions on Cybernetics, vol. 51, no. 7, pp. 3417-3428, Jul. 2021.

  12. Min Wang, Kathryn Kasmarik, Anastasios Bezerianos, Kay Chen Tan, Hussein Abbass, “On the Channel Density of EEG Signals for Reliable Biometric Recognition”, Pattern Recognition Letters, vol. 147, pp. 134-141, Jul. 2021.

  13. Cheng He, Ran Cheng, Ye Tian, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin, “Paired Offspring Generation for Constrained Large-scale Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 25, no. 3, pp. 448-462, Jun. 2021.

  14. Su Nguyen, Dhananjay Thiruvady, Mengjie Zhang, and Kay Chen Tan, “A Genetic Programming Approach for Evolving Variable Selectors in Constraint Programming”, IEEE Transactions on Evolutionary Computation, vol. 25, no. 3, pp. 492-507, Jun. 2021.

  15. Tingfang Wu, Linqiang Pan, Qiang Yu, Kay Chen Tan, “Numerical Spiking Neural P Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, pp. 2443-2457, Jun. 2021.

  16. Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, and Yaochu Jin, “Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)”, IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3129-3142, Jun. 2021.

  17. Ye Tian, Chang Lu, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin, “Solving Large-Scale Multi-Objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks”, IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3115-3128, Jun. 2021.

  18. Jiabin Lin, Hai-Lin Liu, Kay Chen Tan, and Fangqing Gu, “An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization”, IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3238-3248, Jun. 2021.

  19. Liang Feng, Yuxiao Huang, Lei Zhou, Jinghui Zhong, Abhishek Gupta, Ke Tang, and Kay Chen Tan, “Explicit Evolutionary Multi-tasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem”, IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3143-3156, Jun. 2021.

  20. Linqiang Pan, Lianghao Li, Ran Cheng, Cheng He, and Kay Chen Tan, “Manifold Learning-Inspired Mating Restriction for Evolutionary Multi-Objective Optimization with Complicated Pareto Sets”, IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3325-3337, Jun. 2021.

  21. Liang Feng, Lei Zhou, Abhishek Gupta, Jinghui Zhong, Zexuan Zhu, Kay Chen Tan and Kai Qin, “Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking”, IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3171-3184, Jun. 2021.

  22. Ye Tian, Ruchen Liu, Xingyi Zhang, Haiping Ma, Kay Chen Tan, and Yaochu Jin, “A Multi-Population Evolutionary Algorithm for Solving Large-Scale Multi-Modal Multi-Objective Optimization Problems”, IEEE Transactions on Evolutionary Computation, vol. 25, no. 3, pp. 405-418, Jun. 2021.

  23. Xun Zhou, A. K. Qin, Maoguo Gong, and Kay Chen Tan, “A Survey on Evolutionary Construction of Deep Neural Networks”, IEEE Transactions on Evolutionary Computation, vol. 25, no. 5, pp. 894-912, May 2021.

  24. Lei Zhou, Liang Feng, Kay Chen Tan, Jinghui Zhong, Zexuan Zhu, Kai Liu, and Chao Chen, “Towards Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation”, IEEE Transactions on Cybernetics, vol. 51, no. 5, pp. 2563-2576, May 2021.

  25. Jiaxin Chen, Jinliang Ding, Kay Chen Tan, and Qingda Chen, “A Decomposition-based Evolutionary Algorithm for Scalable Multi/Many-objective Optimization”, Memetic Computing, vol. 13, pp. 413-432, Apr. 2021.

  26. Su Nguyen, Mengjie Zhang, Damminda Alahakoon and Kay Chen Tan, “People-centric Evolutionary System for Dynamic Production Scheduling”, IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1403-1416, Mar. 2021.

  27. Lyuyang Tong, Bo Du, Rong Liu, Liangpei Zhang and Kay Chen Tan, “Hyperspectral Endmember Extraction by (+) Multiobjective Differential Evolution Algorithm Based on Ranking Multiple Mutations”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2352-2364, Mar. 2021.

  28. Qiang Yu, Yanli Yao, Longbiao Wang, Huajin Tang, Jianwu Dang, Kay Chen Tan, "Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning", IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 2, pp. 625-638, Feb. 2021.

  29. Kay Chen Tan, Liang Feng, and Min Jiang, "Evolutionary Transfer Optimization - A New Frontier in Evolutionary Computation Research", IEEE Computational Intelligence Magazine, vol. 16, no. 1, pp. 22-33, Feb. 2021.

  30. H. Hong, K. Ye, M. Jiang, and K. C. Tan, “Solving large-scale multi-objective optimization via probabilistic prediction model”, International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, Chian, Marc.28-31, pp. 605-616, 2021.

  31. Y. Feng, L. Feng, Y. Hou, K. C. Tan and S. Kwong, “EMT-ReMO: Evolutionary Multitasking for High-Dimensional Multi-Objective Optimization via Random Embedding”, IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, Jun. 28-Jul. 01, pp. 1672-1679, 2021.

  32. X. Zhou, A. K. Qin, Y. N. Sun, and K. C. Tan, “A Survey of Advances in Evolutionary Neural Architecture Search”, IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, Jun. 28-Jul. 01, pp. 950-957, 2021.

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