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Transfer Optimization is an incipient research area dedicated to solvingmultiple optimization tasks simultaneously. Among the different approaches thatcan address this problem effectively, Evolutionary Multitasking resorts toconcepts from Evolutionary Computation to solve multiple problems within asingle search process. In this paper we introduce a novel adaptivemetaheuristic algorithm to deal with Evolutionary Multitasking environmentscoined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm(AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms inorder to exchange knowledge among the optimization problems underconsideration. Furthermore, our approach is able to explain by itself thesynergies among tasks that were encountered and exploited during the search,which helps us to understand interactions between related optimization tasks. Acomprehensive experimental setup is designed to assess and compare theperformance of AT-MFCGA to that of other renowned evolutionary multitaskingalternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarioscomposed of 20 instances of 4 combinatorial optimization problems, yielding thelargest discrete multitasking environment solved to date. Results areconclusive in regard to the superior quality of solutions provided by AT-MFCGAwith respect to the rest of the methods, which are complemented by aquantitative examination of the genetic transferability among tasks throughoutthe search process.

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking