Osaba, EnekoDel Ser, JavierSuganthan, Ponnuthurai N.2024-07-242024-07-242022-12Osaba , E , Del Ser , J & Suganthan , P N 2022 , ' Evolutionary Multitask Optimization : Fundamental research questions, practices, and directions for the future ' , Swarm and Evolutionary Computation , vol. 75 , 101203 . https://doi.org/10.1016/j.swevo.2022.1012032210-6502https://hdl.handle.net/11556/3961Publisher Copyright: © 2022 Elsevier B.V.Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: (i) the plausibility and practical applicability of this paradigm; (ii) the novelty of some proposed multitasking methods; and (iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.enginfo:eu-repo/semantics/openAccessjournal article10.1016/j.swevo.2022.101203Evolutionary multitaskingMultifactorial evolutionary algorithmMultitasking optimizationTransfer OptimizationGeneral Computer ScienceGeneral Mathematicshttp://www.scopus.com/inward/record.url?scp=85141922374&partnerID=8YFLogxK