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A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking
2020
Frontiers in Neuroscience
Different from conventional single-task optimization, the recently proposed multitasking optimization (MTO) simultaneously deals with multiple optimization tasks with different types of decision variables. MTO explores the underlying similarity and complementarity among the component tasks to improve the optimization process. The well-known multifactorial evolutionary algorithm (MFEA) has been successfully introduced to solve MTO problems based on transfer learning. However, it uses a simple
doi:10.3389/fnins.2019.01408
pmid:31992969
pmcid:PMC6971124
fatcat:hp3fwedowba3thjmx3t3yxxuza