计算机工程与应用2026，Vol.62Issue(16)：160-169,10.DOI:10.3778/j.issn.1002-8331.2506-0152
多任务局部进化学习——消减负迁移与参数冗余
Multi-Task Local Evolutionary Learning:Reducing Negative Transfer and Parameter Redundancy
摘要
Abstract
Local evolutionary learning(LEL)is proposed to mitigate parameter negative transfer caused by low inter-task correlation in multi-task learning(MTL).Drawing on bionic evolutionary learning,LEL constructs local evolutionary units that incorporate inter-task correlation information to perform localized evolution and parameter updates during model training,thereby avoiding ineffective parameter interference among tasks.Upon introduction of a new task,LEL dynami-cally activates existing units according to their correlation with the new task,and applies adaptive evolutionary strategies to optimize the learning process of each subtask.Compared with existing MTL models,LEL preserves parameter sharing among highly correlated tasks while effectively reducing negative transfer between low-correlation tasks.Experimental results on multiple real-world datasets demonstrate that LEL enhances collaborative learning ability,improves generaliza-tion performance,and reduces parameter redundancy.关键词
多任务学习/负迁移/深度学习/局部进化学习Key words
multi-task learning/negative transfer/deep learning/local evolutionary learning分类
信息技术与安全科学引用本文复制引用
孟庆鑫,虎倩,雷霞,刘建伟..多任务局部进化学习——消减负迁移与参数冗余[J].计算机工程与应用,2026,62(16):160-169,10.基金项目
国家自然科学基金青年科学基金项目(62406299). （62406299）
