Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population's implicit parallelism to jointly solve a set of tasks, hence creating avenues for skills transfer between them. Despite it being early days, the idea of EMT has begun to show promise in a range of real-world applications. In the backdrop of recent advances, the contribution of this paper is twofold. We first present a review of several application-oriented explorations of EMT in the literature, assimilating them into half a dozen broad categories according to their respective application areas. Within each category, the fundamental motivations for multitasking are discussed, together with an illustrative case study. Second, we present a set of recipes by which general problem formulations of practical interest, those that cut across different disciplines, could be transformed in the new light of EMT. We intend our discussions to not only underscore the practical utility of existing EMT methods, but also spark future research toward novel algorithms crafted for real-world deployment.

中文翻译：

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进化多任务处理的六个实际应用等等

直到最近，在进化计算中很少探索在不同的优化问题实例（或任务）之间转移进化技能的潜力。进化多任务 (EMT) 的概念填补了这一空白。它释放了群体的隐含并行性以共同解决一组任务，从而为他们之间的技能转移创造了途径。尽管还处于早期阶段，但 EMT 的想法已开始在一系列实际应用中显示出前景。在最新进展的背景下，本文的贡献是双重的。我们首先回顾了文献中几个面向应用的 EMT 探索，根据它们各自的应用领域将它们归入六个大类。在每个类别中，讨论了多任务处理的基本动机，连同说明性案例研究。其次，我们提出了一组方法，通过这些方法，可以在 EMT 的新视角下转换具有实际意义的一般问题，即那些跨越不同学科的问题。我们的讨论不仅要强调现有 EMT 方法的实际效用，还要激发未来对为现实世界部署而设计的新算法的研究。