Multifactorial Evolution: Toward Evolutionary Multitasking

By Abhishek Gupta, Yew-Soon Ong, Liang Feng. IEEE Transactions on Evolutionary Computation, 2016

845 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f4fbbda09a5a923d466d6f67f9bcd9af5a0895f7

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Citations in the corpus

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, background): “Problems: Multitasking optimization [47]–[49] provides an effective means to transfer knowledge between different problems to speed up optimization.”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, background): “While sequential transfer optimization learns from historical problems, multitask optimization [114], [115], [117] simultaneously solves multiple similar problems.”
Evolutionary Multitasking via Explicit Autoencoding (2019, methodology): “For example, solution sa is considered to dominate sb in multitasking sense simply if φa > φb [14].”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “An example of this translations is the Random-Keys based encoding, often used in Transfer Optimization environments [67, 68].”
Insights on Transfer Optimization: Because Experience is the Best Teacher (2018, methodology): “In contrast, nature-inspired techniques, albeit less data efficient, provide considerable flexibility with the interplay of evolutionary mechanisms and model-based transfer [16], [24].”
A Survey on Evolutionary Constrained Multiobjective Optimization (2023, background): “1) CMOPs can be solved on the principle of multi-task optimization [128]–[130].”
Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II (2020, background): “Recently proposed, the so-called MFEA [18] leverages on the implicit parallelism of population-based search to solve multiple problems simultaneously with omnidirectional trans-”
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, background): “It is worth mentioning that a similar idea has been adopted in multifactorial evolution [42], which employs a uniﬁed representation to simultaneously solve different problems with the assistance of the implicit transfer of useful genetic material between problems [43].”
Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, methodology): “of evolutionary multitasking leads to implicit geneticallyencoded information transfer across optimization tasks, which often facilitates improved convergence characteristics [21].”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “Evolutionary multitasking is a very promising example of ETO, demonstrating that this might be a very valuable concept in the real world application (Gupta et al. 2015).”