Evolutionary Multitasking (EMT) paradigm, an emerging research topic in evolutionary computation, has been successfully applied in solving high-dimensional feature selection (FS) problems recently. However, existing EMT-based FS methods suffer from several limitations, such as a single mode of multitask generation, conducting the same generic evolutionary search for all tasks, relying on implicit transfer mechanisms through sole solution encodings, and employing single-objective transformation, which result in inadequate knowledge acquisition, exploitation, and transfer. To this end, this paper develops a novel EMT framework for multiobjective high-dimensional feature selection problems, namely MO-FSEMT. In particular, multiple auxiliary tasks are constructed by distinct formulation methods to provide diverse search spaces and information representations and then simultaneously addressed with the original task through a multi-slover-based multitask optimization scheme. Each task has an independent population with task-specific representations and is solved using separate evolutionary solvers with different biases and search preferences. A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions during the search process. Comprehensive experimental results demonstrate that our MO-FSEMT framework can achieve overall superior performance compared to the state-of-the-art FS methods on 26 datasets. Moreover, the ablation studies verify the contributions of different components of the proposed MO-FSEMT.

翻译：进化多任务（EMT）范式作为进化计算领域的新兴研究方向，近期已被成功应用于高维特征选择（FS）问题的求解。然而，现有基于EMT的特征选择方法存在若干局限性，例如多任务生成模式单一、对所有任务采用相同通用进化搜索策略、依赖仅通过单一解编码实现的隐式迁移机制，以及采用单目标转化方法，导致知识获取、利用与迁移的不充分。为此，本文针对多目标高维特征选择问题，提出了一种新型EMT框架，即MO-FSEMT。具体而言，通过不同构建方法生成多个辅助任务，以提供多样化的搜索空间与信息表征，随后通过基于多求解器的多任务优化方案与原始任务协同求解。每个任务拥有独立种群及任务特异性表征，并采用具备不同偏向与搜索偏好的独立进化求解器进行求解。同时设计了一种任务特异性知识迁移机制，充分利用各任务的优势信息，在搜索过程中实现高质量方案的发现与有效传递。综合实验结果表明，在26个数据集上，我们提出的MO-FSEMT框架能够实现优于现有最优特征选择方法的总体性能。此外，消融实验验证了MO-FSEMT各组成部分的贡献。