Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average.

进化多任务处理是进化计算领域中的新兴课题。通过采用有效地发现有用的遗传物质并将其从一项任务隐式转移到另一项任务的方法，它可以使用一个进化计算同时处理多个优化任务。受进化多任务处理思想的启发，它还可用于模糊系统（FS）的优化问题。通过在不同的FS之间交换优化经验和知识，有望提高FS优化的速度和效率，并将其应用于对运行时间和结果准确性有更高要求的FS优化任务。此外，利用简单FS优化任务的经验和知识来促进复杂FS的优化，它可以解决由大型，复杂的FS优化问题引发的高耗时和高成本，并提高了其在大型复杂的模糊控制优化问题中应用的可行性。与一般的多任务学习不同，FS优化的多任务学习有其自身的特点。因此，基于进化多任务的思想和FS优化的多任务学习的特点，提出了一种多任务遗传模糊系统的通用框架，以有效解决模糊系统的多任务优化问题。设计并实现了具有完全重叠的三角形隶属度函数的Mamdani模糊系统的多任务进化优化算法（FOTMF-M-MTGFS）。与遗传模糊系统（GFS）的比较研究，