  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 is intended to spark future research towards
crafting novel algorithms for real-world deployment.

Neural and Evolutionary Computing;Artificial Intelligence;Emerging Technologies