Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking
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.
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