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<p>Evolutionary multitasking (EMT) has shown strong performance in high-dimensional feature selection (FS) by building implicit parallelism bridges between different tasks. However, the knowledge transfer in existing EMT-based FS methods has the following problems: the knowledge for each task dynamically changes throughout the evolutionary process; the quality of knowledge across tasks may also vary significantly in the same iteration. If the dynamic characteristics of the task are ignored and a static knowledge transfer mechanism is adopted, this can result in the transfer of useless or negative knowledge, thereby reducing knowledge transfer efficiency and potentially misguiding or disrupting the search direction of the target task. To this end, we propose a novel EMT-based framework, namely MTABC. Specifically, three filter-based methods are used to generate low-dimensional search landscapes with multiple types of knowledge to enhance search diversity and reduce the search space. Secondly, an adaptive weight-based knowledge transfer mechanism adjusts the weight of each task based on real-time performance improvement, ensuring flexible and efficient knowledge transfer. In addition, we employ a modified gbest-guided Artificial Bee Colony (GABC) algorithm as the core optimizer, which is further enhanced by a Differential Evolution (DE) based elite bees secondary search strategy during the onlooker bee phase to improve exploitation and discover better solutions. Extensive experimental results confirm that our MTABC framework surpasses state-of-the-art FS methods on 15 datasets. Furthermore, the contributions of each component of the proposed MTABC are verified through ablation experiments.</p>

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Evolutionary multitasking by dynamic adaptive knowledge transfer for high-dimensional feature selection

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Yujia Wei,
  • Hongkun Lin,
  • Binbin Chen,
  • Juehan Lu,
  • Shiguo Huang,
  • Xiaolin Li

摘要

Evolutionary multitasking (EMT) has shown strong performance in high-dimensional feature selection (FS) by building implicit parallelism bridges between different tasks. However, the knowledge transfer in existing EMT-based FS methods has the following problems: the knowledge for each task dynamically changes throughout the evolutionary process; the quality of knowledge across tasks may also vary significantly in the same iteration. If the dynamic characteristics of the task are ignored and a static knowledge transfer mechanism is adopted, this can result in the transfer of useless or negative knowledge, thereby reducing knowledge transfer efficiency and potentially misguiding or disrupting the search direction of the target task. To this end, we propose a novel EMT-based framework, namely MTABC. Specifically, three filter-based methods are used to generate low-dimensional search landscapes with multiple types of knowledge to enhance search diversity and reduce the search space. Secondly, an adaptive weight-based knowledge transfer mechanism adjusts the weight of each task based on real-time performance improvement, ensuring flexible and efficient knowledge transfer. In addition, we employ a modified gbest-guided Artificial Bee Colony (GABC) algorithm as the core optimizer, which is further enhanced by a Differential Evolution (DE) based elite bees secondary search strategy during the onlooker bee phase to improve exploitation and discover better solutions. Extensive experimental results confirm that our MTABC framework surpasses state-of-the-art FS methods on 15 datasets. Furthermore, the contributions of each component of the proposed MTABC are verified through ablation experiments.