Multiobjective Multifactorial Optimization in Evolutionary Multitasking

By Abhishek Gupta, Yew-Soon Ong, Liang Feng, Kay Chen Tan. IEEE Transactions on Cybernetics, 2017

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Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II (2020, background): “The importance of search space unification in multitasking is to encompass all the individual search spaces of different optimization tasks such that we have a shared platform on which the transfer of knowledge can seamlessly take place [7], [19], [42].”
Evolutionary Multitasking via Explicit Autoencoding (2019, methodology): “On task T2 of PILS, the proposed method and MOMFEA obtained the IGD value of 6.26E − 2 and 1.11E−2, respectively, while the single-task NSGAII only researched the IGD 6.36E−1.”
Insights on Transfer Optimization: Because Experience is the Best Teacher (2018, background): “Even the benefits of evolutionary multitasking have been demonstrated in the context of engineering design [91], [92].”