  Multitasking optimization is a recently introduced paradigm, focused on the
simultaneous solving of multiple optimization problem instances (tasks). The
goal of multitasking environments is to dynamically exploit existing
complementarities and synergies among tasks, helping each other through the
transfer of genetic material. More concretely, Evolutionary Multitasking (EM)
regards to the resolution of multitasking scenarios using concepts inherited
from Evolutionary Computation. EM approaches such as the well-known
Multifactorial Evolutionary Algorithm (MFEA) are lately gaining a notable
research momentum when facing with multiple optimization problems. This work is
focused on the application of the recently proposed Multifactorial Cellular
Genetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem
(CVRP). In overall, 11 different multitasking setups have been built using 12
datasets. The contribution of this research is twofold. On the one hand, it is
the first application of the MFCGA to the Vehicle Routing Problem family of
problems. On the other hand, equally interesting is the second contribution,
which is focused on the quantitative analysis of the positive genetic
transferability among the problem instances. To do that, we provide an
empirical demonstration of the synergies arisen between the different
optimization tasks.
