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

• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. Synergies between tasks are analyzed along the search in a dynamic way. 4 different combinatorial optimization problems have been considered. 11 multitasking scenarios solved comprised by 5 to 20 instances. Proposed AT-MFCGA compared with MFEA, MFEA-II MFCGA. Transfer Optimization an incipient research area dedicated solving multiple simultaneously. Among approaches that can address this problem effectively, Multitasking resorts concepts from Computation solve within single process. In paper we introduce novel algorithm deal environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). relies on cellular automata implement mechanisms order exchange knowledge among under consideration. Furthermore, our approach able explain itself synergies were encountered exploited during search, which helps us understand interactions related tasks. A comprehensive experimental setup designed assess compare performance of other renowned alternatives (MFEA MFEA-II). Experiments comprise composed instances problems, yielding largest discrete environment date. Results conclusive regard superior quality solutions provided respect rest methods, complemented quantitative examination genetic transferability throughout