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Multitasking optimization is a recently introduced paradigm, focused on thesimultaneous solving of multiple optimization problem instances (tasks). Thegoal of multitasking environments is to dynamically exploit existingcomplementarities and synergies among tasks, helping each other through thetransfer of genetic material. More concretely, Evolutionary Multitasking (EM)regards to the resolution of multitasking scenarios using concepts inheritedfrom Evolutionary Computation. EM approaches such as the well-knownMultifactorial Evolutionary Algorithm (MFEA) are lately gaining a notableresearch momentum when facing with multiple optimization problems. This work isfocused on the application of the recently proposed Multifactorial CellularGenetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem(CVRP). In overall, 11 different multitasking setups have been built using 12datasets. The contribution of this research is twofold. On the one hand, it isthe first application of the MFCGA to the Vehicle Routing Problem family ofproblems. On the other hand, equally interesting is the second contribution,which is focused on the quantitative analysis of the positive genetictransferability among the problem instances. To do that, we provide anempirical demonstration of the synergies arisen between the differentoptimization tasks.

On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking