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  1. No Access

    Chapter and Conference Paper

    A Stacked Autoencoder Based Meta-Learning Model for Global Optimization

    As optimization problems continue to become more complex, previous studies have demonstrated that algorithm performance varies depending on the specific problem being addressed. Thus, this study proposes an ad...

    Yue Ma, Yongsheng Pang, Shuxiang Li… in International Conference on Neural Computi… (2023)

  2. No Access

    Chapter and Conference Paper

    Data-Driven Recommendation Model with Meta-learning Autoencoder for Algorithm Selection

    To improve the efficiency of problem-solving for complex optimization problems, meta-learning was applied in algorithm selection to choose the most appropriate algorithm recently. However, the common meta-lear...

    **anghua Chu, Yongsheng Pang, Jiayun Wang… in Neural Computing for Advanced Applications (2022)