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Towards evolution-based multi-level collaborative multi-task sparse learning
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AI
DOI：10.1016/j.swevo.2026.102385.png)
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Abstract
En 中文
The sparsity of solutions in certain problems typically exhibits strong correlations with the input data, which highlights the need for generalized models to facilitate effective learning. Multi-task learning rises to this challenge by incorporating auxiliary tasks, enabling the transfer of information across tasks to enhance model learning and representation. However, auxiliary tasks do not always contribute equally to the improvement of the main task. Given the nonlinear relationships between tasks, accurately capturing these interactions is challenging. Additionally, the optimal collaborative combination weights may be different as the main task changes. To address the above problems, this paper proposes an evolution-based multi-level collaborative multi-task sparse learning framework (EMCMSL), which integrates evolutionary optimization into the sparse learning process to enable both intra-model and inter-model task collaboration. At the intra-model level, auxiliary tasks are incorporated through shared network parameters to jointly enhance feature representation. At the inter-model level, a tailored evolutionary multi-task optimization mechanism is employed to search for optimal collaborative weight configurations across multiple sparse problems. This collaboration enables EMCMSL to automatically discover beneficial cooperative structures for different main tasks while mitigating negative transfer. The proposed framework is evaluated on two main tasks: sparse reconstruction and denoising. The experimental results validate the effectiveness of multi-level task collaboration and demonstrate that the proposed algorithm can achieve good results on different data.
Keywords:
Multi-task learning
Sparse learning
Evolutionary optimization
Task collaboration
Feature representation
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