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. First, a review of several application-oriented explorations of EMT in the literature is presented; the works are assimilated into half a dozen broad categories according to their respective application domains. Each of these six categories elaborates fundamental motivations to multitask, and contains a representative experimental study (referred from the literature). Second, a set of recipes is provided showing how problem formulations of general interest, those that cut across different disciplines, could be transformed in the new light of EMT. Our discussions emphasize the many practical use-cases of EMT, and are intended to spark future research towards crafting novel algorithms for real-world deployment.

中文翻译：

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进化多任务处理的六种实际应用等等


直到最近，进化计算中还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。进化多任务处理（EMT）的概念填补了这一空白。它释放了群体的隐含并行性，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 EMT 的想法还处于早期阶段，但它已经开始在一系列实际应用中展现出希望。在最新进展的背景下，本文的贡献是双重的。首先，回顾了文献中关于EMT的几种应用型探索；这些作品根据各自的应用领域被分为六大类。这六个类别中的每一个都详细阐述了多任务的基本动机，并包含代表性的实验研究（参考文献）。其次，提供了一组方法，展示了如何根据 EMT 的新视角来转变普遍感兴趣的问题表述（那些跨越不同学科的问题）。我们的讨论强调了 EMT 的许多实际用例，旨在激发未来的研究，为现实世界的部署打造新颖的算法。