Evolutionary Multitasking for Multiobjective Feature Selection in Classification

Evolutionary multiobjective optimization has shown success in feature selection. However, existing methods often address these tasks independently, disregarding their potential interconnections and shared knowledge. On the other hand, evolutionary multitasking (EMT) has been utilized to address multiple related tasks simultaneously and transfer common knowledge. However, most EMTL-based feature selection methods prioritize a single task, and treat it as the main task and the other tasks as auxiliary or secondary tasks. To overcome this limitation, we propose a novel multiobjective feature selection method based on EMT in this article. The new method introduces a novel representation that consolidates the solutions of multiple interconnected feature selection tasks into a single solution. It enables these tasks to share a common population, thereby enhancing the effectiveness and efficiency of transferring common knowledge across them. In addition, a novel searching method is devised to facilitate the evolution of the population across multiple tasks, enabling effective knowledge transfer between them. Finally, a transformation method is introduced to transfer valuable genes among the solutions for multiple tasks, thereby enhancing the overall performance of the proposed algorithm when confronted with tasks characterized by distinct features. This method effectively addresses multiple feature selection tasks simultaneously, offering a comprehensive solution to the aforementioned issue. Compared with four single-task multiobjective feature selection methods and a state-of-the-art EMT-based feature selection method, the proposed method demonstrates superior feature selection performance across the majority of benchmark datasets.