  In this work we consider multitasking in the context of solving multiple
optimization problems simultaneously by conducting a single search process. The
principal goal when dealing with this scenario is to dynamically exploit the
existing complementarities among the problems (tasks) being optimized, helping
each other through the exchange of valuable knowledge. Additionally, the
emerging paradigm of Evolutionary Multitasking tackles multitask optimization
scenarios by using as inspiration concepts drawn from Evolutionary Computation.
The main purpose of this survey is to collect, organize and critically examine
the abundant literature published so far in Evolutionary Multitasking, with an
emphasis on the methodological patterns followed when designing new algorithmic
proposals in this area (namely, multifactorial optimization and
multipopulation-based multitasking). We complement our critical analysis with
an identification of challenges that remain open to date, along with promising
research directions that can stimulate future efforts in this topic. Our
discussions held throughout this manuscript are offered to the audience as a
reference of the general trajectory followed by the community working in this
field in recent times, as well as a self-contained entry point for newcomers
and researchers interested to join this exciting research avenue.
