Multi-Objective Genetic Programming Assisted Stochastic Deep Reinforcement Learning for Dynamic Knowledge Integration in Transportation Networks

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@Article{Xue:TITS,

• author = "Xingsi Xue and Guojun Mao and Saru Kumari and Zhonghua Liu",
• title = "Multi-Objective Genetic Programming Assisted Stochastic Deep Reinforcement Learning for Dynamic Knowledge Integration in Transportation Networks",
• journal = "IEEE Transactions on Intelligent Transportation Systems",
• keywords = "genetic algorithms, genetic programming, Real-time systems, Accuracy, Complexity theory, Optimisation, Dynamic programming, Deep reinforcement learning, Soft sensors, Probabilistic logic, Global Positioning System, Faces, Transportation network, dynamic knowledge integration, stochastic deep reinforcement learning, multi-objective genetic programming",
• ISSN = "1558-0016",
• DOI = "doi:10.1109/TITS.2025.3545572",
• abstract = "Transportation Networks (TNs) play a critical role in economic and social systems, yet the dynamic nature and inherent heterogeneity of TN data pose challenges for Dynamic Knowledge Integration (DKI). Traditional approaches for matching entities from different knowledge bases often struggle with the complexity and diversity of TN data, which varies across systems and sources such as traffic sensors and GPS devices. To address this issue, this paper proposes a novel Multi-Objective Genetic Programming assisted Stochastic Deep Reinforcement Learning (MOGP-SDRL) for DKI in TNs. Unlike existing methods, the proposed framework combines SDRL and MOGP to achieve superior efficiency, accuracy and adaptability in handling heterogeneous TN data. First, a novel SDRL framework is designed to automate and optimise the selection of Similarity Features (SFs) for entity matching. This framework incorporates a probabilistic action selection mechanism, which enhances exploration during the SF selection process. Second, a novel MOGP is presented to construct high-quality, diverse SFs by exploring non-dominated feature ensembles, enhancing both accuracy and adaptability in matching results, leading to more accurate and adaptable matching results compared to conventional methods. Lastly, new approximate evaluation metrics are developed to assess alignment quality without relying on predefined entity alignments, guiding the optimisation process. Experimental evaluations on OAEI's knowledge graph (KG) dataset and five pairs of real-world TN dataset demonstrate the effectiveness of the MOGP-SDRL framework, which consistently produces high-quality matching results and achieves significant improvements in both accuracy and robustness over existing approaches.",
• notes = "Also known as \cite{10908447}",

}

Genetic Programming entries for Xingsi Xue Guojun Mao Saru Kumari Zhonghua Liu

Citations