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Multitask optimization

Multi-task optimization is a paradigm in the optimization literature that focuses on solving multiple self-contained tasks simultaneously.[1][2] The paradigm has been inspired by the well-established concepts of transfer learning[3] and multi-task learning[4] in predictive analytics.

The key motivation behind multi-task optimization is that if optimization tasks are related to each other in terms of their optimal solutions or the general characteristics of their function landscapes,[5] the search progress can be transferred to substantially accelerate the search on the other.

The success of the paradigm is not necessarily limited to one-way knowledge transfers from simpler to more complex tasks. In practice an attempt is to intentionally solve a more difficult task that may unintentionally solve several smaller problems.[6]

There is a direct relationship between multitask optimization and multi-objective optimization.[7]

  1. ^ Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang (2018). "Insights on Transfer Optimization: Because Experience is the Best Teacher". IEEE Transactions on Emerging Topics in Computational Intelligence. 2: 51–64. doi:10.1109/TETCI.2017.2769104. hdl:10356/147980. S2CID 11510470.
  2. ^ Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang (2016). "Multifactorial Evolution: Toward Evolutionary Multitasking". IEEE Transactions on Evolutionary Computation. 20 (3): 343–357. doi:10.1109/TEVC.2015.2458037. hdl:10356/148174. S2CID 13767012.
  3. ^ Pan, Sinno Jialin; Yang, Qiang (2010). "A Survey on Transfer Learning". IEEE Transactions on Knowledge and Data Engineering. 22 (10): 1345–1359. doi:10.1109/TKDE.2009.191. S2CID 740063.
  4. ^ Caruana, R., "Multitask Learning", pp. 95-134 in Sebastian Thrun, Lorien Pratt (eds.) Learning to Learn, (1998) Springer ISBN 9780792380474
  5. ^ Cheng, Mei-Ying; Gupta, Abhishek; Ong, Yew-Soon; Ni, Zhi-Wei (2017). "Coevolutionary multitasking for concurrent global optimization: With case studies in complex engineering design". Engineering Applications of Artificial Intelligence. 64: 13–24. doi:10.1016/j.engappai.2017.05.008. S2CID 13767210.
  6. ^ Cabi, Serkan; Sergio Gómez Colmenarejo; Hoffman, Matthew W.; Denil, Misha; Wang, Ziyu; Nando de Freitas (2017). "The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously". arXiv:1707.03300 [cs.AI].
  7. ^ J. -Y. Li, Z. -H. Zhan, Y. Li and J. Zhang, "Multiple Tasks for Multiple Objectives: A New Multiobjective Optimization Method via Multitask Optimization," in IEEE Transactions on Evolutionary Computation, doi:10.1109/TEVC.2023.3294307

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