Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel and easy-to-implement multi-tasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.
翻译:多任务学习利用辅助数据或相关任务的知识来便利在新任务中学习。多任务优化应用多任务学习优化以优化研究如何同时有效和高效地解决多重优化问题。进化式多任务或多因素优化是一个新兴的多任务优化子领域,它综合了进化计算和多任务学习。本文提出一个新的和易于实施的多任务遗传算法(MTGA),它通过估计和使用其中的偏差来很好地应对显著不同的优化任务。在文献中,八种最先进的单项和多任务方法的比较研究显示,平均而言,MTGA所有方法都优于这些方法,计算成本低于其中的六项。在MTGA的基础上,还提出了烟雾系统设计的同步优化战略。关于对组合式-1和间隔型二型模糊逻辑控制器进行同步优化的实验表明,双罐水控制时,MTGA可以找到比其他方法更好的模糊逻辑控制器。