Osaba, EnekoMartinez, Aritz D.Lobo, Jesus L.Laña, IbaiSer, Javier Del2020-09-20Osaba , E , Martinez , A D , Lobo , J L , Laña , I & Ser , J D 2020 , On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking . in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 . , 9294497 , 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 , Institute of Electrical and Electronics Engineers Inc. , 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 , Rhodes , Greece , 20/09/20 . https://doi.org/10.1109/ITSC45102.2020.9294497conference9781728141497Publisher Copyright: © 2020 IEEE.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.enginfo:eu-repo/semantics/openAccessconference output10.1109/ITSC45102.2020.9294497Artificial IntelligenceDecision Sciences (miscellaneous)Information Systems and ManagementModeling and SimulationEducationhttp://www.scopus.com/inward/record.url?scp=85099643226&partnerID=8YFLogxK