Outputs (9)

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.

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.

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, 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.

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.

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.