Multifactorial evolutionary algorithm with adaptive transfer strategy based on decision tree

Abstract Multifactorial optimization (MFO) is a kind of problem that has attracted considerable attention in recent years. The multifactorial evolutionary algorithm utilizes the implicit genetic transfer mechanism characterized by knowledge to conduct multitasking simultaneously. Therefore, effectiveness significantly affects performance algorithm. To achieve positive transfer, this paper proposed an with adaptive strategy based on decision tree (EMT-ADT). evaluate useful contained transferred individuals, defines evaluation indicator quantify ability each individual. Furthermore, constructed predict individuals. Based prediction results, promising positive-transferred individuals are selected knowledge, which can effectively improve Finally, CEC2017 MFO benchmark problems, WCCI20-MTSO and WCCI20-MaTSO problems used verify EMT-ADT. Experimental results demonstrate competiveness EMT-ADT compared some state-of-the-art algorithms.