Multitask optimization
Optimizing the solving of multiple self-contained tasks simultaneously / From Wikipedia, the free encyclopedia
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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.
This article provides insufficient context for those unfamiliar with the subject. (November 2021) |
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]