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Multitasking optimization is an incipient research area which is latelygaining a notable research momentum. Unlike traditional optimization paradigmthat focuses on solving a single task at a time, multitasking addresses howmultiple optimization problems can be tackled simultaneously by performing asingle search process. The main objective to achieve this goal efficiently isto exploit synergies between the problems (tasks) to be optimized, helping eachother via knowledge transfer (thereby being referred to as TransferOptimization). Furthermore, the equally recent concept of EvolutionaryMultitasking (EM) refers to multitasking environments adopting concepts fromEvolutionary Computation as their inspiration for the simultaneous solving ofthe problems under consideration. As such, EM approaches such as theMultifactorial Evolutionary Algorithm (MFEA) has shown a remarkable successwhen dealing with multiple discrete, continuous, single-, and/ormulti-objective optimization problems. In this work we propose a novelalgorithmic scheme for Multifactorial Optimization scenarios - theMultifactorial Cellular Genetic Algorithm (MFCGA) - that hinges on conceptsfrom Cellular Automata to implement mechanisms for exchanging knowledge amongproblems. We conduct an extensive performance analysis of the proposed MFCGAand compare it to the canonical MFEA under the same algorithmic conditions andover 15 different multitasking setups (encompassing different referenceinstances of the discrete Traveling Salesman Problem). A further contributionof this analysis beyond performance benchmarking is a quantitative examinationof the genetic transferability among the problem instances, eliciting anempirical demonstration of the synergies emerged between the differentoptimization tasks along the MFCGA search process.

Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis