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AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to the
simultaneous solving of multiple optimization tasks. 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 for dealing with Evolutionary Multitasking environments
coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm
(AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms for
exchanging 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
understand interactions between related optimization tasks. A comprehensive
experimental setup is designed for assessing and comparing the performance of
AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA
and MFEA-II). Experiments comprise 11 multitasking scenarios composed by 20
instances of 4 combinatorial optimization problems, yielding the largest
discrete multitasking environment solved to date. Results are conclusive in
regards to the superior quality of solutions provided by AT-MFCGA with respect
to the rest of methods, which are complemented by a quantitative examination of
the genetic transferability among tasks along the search process.Comment: 30 pages, 2 figures, under review for its consideration in
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