All Outputs (112)

Heterogeneous Mutual Knowledge Distillation for Wearable Human Activity Recognition (2025)
Journal Article
Xiao, Z., Xing, H., Qu, R., Li, H., Cheng, X., Xu, L., Feng, L., & Wan, Q. (2025). Heterogeneous Mutual Knowledge Distillation for Wearable Human Activity Recognition. IEEE Transactions on Neural Networks and Learning Systems, 1-15. https://doi.org/10.1109/tnnls.2025.3556317

Recently, numerous deep learning algorithms have addressed wearable human activity recognition (HAR), but they often struggle with efficient knowledge transfer to lightweight models for mobile devices. Knowledge distillation (KD) is a popular techniq... Read More about Heterogeneous Mutual Knowledge Distillation for Wearable Human Activity Recognition.

CFFormer: Cross CNN-Transformer channel attention and spatial feature fusion for improved segmentation of heterogeneous medical images (2025)
Journal Article
Li, J., Xu, Q., He, X., Liu, Z., Zhang, D., Wang, R., Qu, R., & Qiu, G. (2026). CFFormer: Cross CNN-Transformer channel attention and spatial feature fusion for improved segmentation of heterogeneous medical images. Expert Systems with Applications, 295, Article 128835. https://doi.org/10.1016/j.eswa.2025.128835

Medical image segmentation plays an important role in computer-aided diagnosis. Existing methods mainly utilize spatial attention to highlight the region of interest. However, due to limitations of medical imaging devices, medical images exhibit sign... Read More about CFFormer: Cross CNN-Transformer channel attention and spatial feature fusion for improved segmentation of heterogeneous medical images.

Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization (2024)
Journal Article
Wang, H., Chen, L., Hao, X., Qu, R., Zhou, W., Wang, D., & Liu, W. (2024). Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization. Swarm and Evolutionary Computation, 91, Article 101763. https://doi.org/10.1016/j.swevo.2024.101763

When tackling large-scale multi-objective problems (LSMOPs), the computational budget could be wasted by traditional offspring generators that explore the search space in a nearly directionless manner, impairing the efficiency of many existing algori... Read More about Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization.

Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments (2024)
Journal Article
Chen, X., Qu, R., Dong, J., Dong, H., & Bai, R. (2024). Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments. Applied Soft Computing, 166, Article 112190. https://doi.org/10.1016/j.asoc.2024.112190

Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algo... Read More about Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments.

Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition (2024)
Presentation / Conference Contribution
Meng, W., Qu, R., & Pillay, N. (2023, July). Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition. Presented at GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Melbourne VIC Australia

The Competition of Machine Learning for Evolutionary Computation for Solving Vehicle Routing Problems (ML4VRP) seeks to bring together machine learning and evolutionary computation communities to propose innovative techniques for vehicle routing prob... Read More about Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition.

A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression (2024)
Presentation / Conference Contribution
Chen, X., Yi, W., Bai, R., Qu, R., & Jin, Y. (2024, June). A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression. Presented at 2024 IEEE Congress on Evolutionary Computation (CEC 2024), Yokohama, Japan

In regression analysis, methodologies range from black-box approaches like artificial neural networks to white-box techniques like symbolic regression. Renowned for its trans-parency and interpretability, symbolic regression has become increasingly p... Read More about A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression.

Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks (2024)
Journal Article
Chen, X., Bai, R., Qu, R., Dong, J., & Jin, Y. (2024). Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks. IEEE Transactions on Evolutionary Computation, https://doi.org/10.1109/tevc.2024.3381042

Efficient truck dispatching is crucial for optimizing container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have gen... Read More about Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks.

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification (2024)
Journal Article
Xiao, Z., Xu, X., Xing, H., Zhao, B., Wang, X., Song, F., Qu, R., & Feng, L. (2024). DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification. IEEE Transactions on Cognitive and Developmental Systems, 16(4), 1445-1461. https://doi.org/10.1109/tcds.2024.3370219

This paper proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.... Read More about DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article
Lin, B., Li, J., Cui, T., Jin, H., Bai, R., Qu, R., & Garibaldi, J. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications, 249, Article 123515. https://doi.org/10.1016/j.eswa.2024.123515

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk (2024)
Journal Article
Wang, H., Bellotti, A., Qu, R., & Bai, R. (2024). Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks, 12(2), Article 31. https://doi.org/10.3390/risks12020031

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot dis... Read More about Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk.

Self-Bidirectional Decoupled Distillation for Time Series Classification (2024)
Journal Article
Xiao, Z., Xing, H., Qu, R., Li, H., Feng, L., Zhao, B., & Yang, J. (2024). Self-Bidirectional Decoupled Distillation for Time Series Classification. IEEE Transactions on Artificial Intelligence, 5(8), 4101-4110. https://doi.org/10.1109/tai.2024.3360180

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model’s performance usually depends on the quality of the semantic information extracted from lower and higher levels within the repres... Read More about Self-Bidirectional Decoupled Distillation for Time Series Classification.

Densely Knowledge-Aware Network for Multivariate Time Series Classification (2024)
Journal Article
Xiao, Z., Xing, H., Qu, R., Feng, L., Luo, S., Dai, P., Zhao, B., & Dai, Y. (2024). Densely Knowledge-Aware Network for Multivariate Time Series Classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4), 2192-2204. https://doi.org/10.1109/tsmc.2023.3342640

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing s... Read More about Densely Knowledge-Aware Network for Multivariate Time Series Classification.

Discrete-Time Survival Models with Neural Networks for Age-Period-Cohort Analysis of Credit Risk (2024)
Preprint / Working Paper
Wang, H., Bellotti, A. G., Qu, R., & Bai, R. (2024). Discrete-Time Survival Models with Neural Networks for Age-Period-Cohort Analysis of Credit Risk

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot dis... Read More about Discrete-Time Survival Models with Neural Networks for Age-Period-Cohort Analysis of Credit Risk.

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition (2023)
Journal Article
Xiao, Z., Tong, H., Qu, R., Xing, H., Luo, S., Zhu, Z., Song, F., & Feng, L. (2025). CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 2690-2704. https://doi.org/10.1109/TNNLS.2023.3344294

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called... Read More about CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition.

Container port truck dispatching optimization using Real2Sim based deep reinforcement learning (2023)
Journal Article
Jin, J., Cui, T., Bai, R., & Qu, R. (2024). Container port truck dispatching optimization using Real2Sim based deep reinforcement learning. European Journal of Operational Research, 315(1), 161-175. https://doi.org/10.1016/J.EJOR.2023.11.038

In marine container terminals, truck dispatching optimization is often considered as the primary focus as it provides crucial synergy between the sea-side operations and yard-side activities and hence can greatly affect the terminal throughput and qu... Read More about Container port truck dispatching optimization using Real2Sim based deep reinforcement learning.

Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching (2023)
Presentation / Conference Contribution
Chen, X., Bao, F., Qu, R., Dong, J., & Bai, R. (2023, September). Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching. Presented at 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain

Efficient truck dispatching is crucial for container port operations. Dynamic container port truck dispatching, a complex online optimization problem, poses significant challenges due to its uncertain and non-linear nature. This paper presents a nove... Read More about Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching.

Automated design of local search algorithms: Predicting algorithmic components with LSTM (2023)
Journal Article
Meng, W., & Qu, R. (2024). Automated design of local search algorithms: Predicting algorithmic components with LSTM. Expert Systems with Applications, 237(Part A), Article 121431. https://doi.org/10.1016/j.eswa.2023.121431

With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective al... Read More about Automated design of local search algorithms: Predicting algorithmic components with LSTM.

Automated design of search algorithms based on reinforcement learning (2023)
Journal Article
Yi, W., & Qu, R. (2023). Automated design of search algorithms based on reinforcement learning. Information Sciences, 649, Article 119639. https://doi.org/10.1016/j.ins.2023.119639

Automated algorithm design has attracted increasing research attention recently in the evolutionary computation community. The main design decisions include selection heuristics and evolution operators in the search algorithms. Most existing studies,... Read More about Automated design of search algorithms based on reinforcement learning.

Deep Contrastive Representation Learning With Self-Distillation (2023)
Journal Article
Xiao, Z., Xing, H., Zhao, B., Qu, R., Luo, S., Dai, P., Li, K., & Zhu, Z. (2024). Deep Contrastive Representation Learning With Self-Distillation. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 3-15. https://doi.org/10.1109/tetci.2023.3304948

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher level... Read More about Deep Contrastive Representation Learning With Self-Distillation.

Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning (2023)
Journal Article
Aickelin, U., Khorshidi, H. A., Qu, R., & Charkhgard, H. (2023). Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning. IEEE Transactions on Evolutionary Computation, 27(4), 746-748. https://doi.org/10.1109/tevc.2023.3292528

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in mac... Read More about Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning.