  Transfer Optimization is an incipient research area dedicated to solving
multiple optimization tasks simultaneously. Among the different approaches that
can address this problem effectively, Evolutionary Multitasking resorts to
concepts from Evolutionary Computation to solve multiple problems within a
single search process. In this paper we introduce a novel adaptive
metaheuristic algorithm to deal with Evolutionary Multitasking environments
coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm
(AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in
order to exchange knowledge among the optimization problems under
consideration. Furthermore, our approach is able to explain by itself the
synergies among tasks that were encountered and exploited during the search,
which helps us to understand interactions between related optimization tasks. A
comprehensive experimental setup is designed to assess and compare the
performance of AT-MFCGA to that of other renowned evolutionary multitasking
alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios
composed of 20 instances of 4 combinatorial optimization problems, yielding the
largest discrete multitasking environment solved to date. Results are
conclusive in regard to the superior quality of solutions provided by AT-MFCGA
with respect to the rest of the methods, which are complemented by a
quantitative examination of the genetic transferability among tasks throughout
the search process.
