Evolutionary Multitask Optimization is a paradigm proposed by [1] in the optimization literature that focuses on solving multiple self-contained tasks at the same time. Inspired by the well-established concepts of transfer learning and multi-task learning in predictive analytics, the key motivation behind multitask 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), then the search progress on one can be transferred to substantially speedup the search on the other. Notably, the success of the paradigm is not necessarily limited to one-way knowledge transfers from simpler to more complex tasks. In fact, in an attempt to intentionally solve a harder task, several simpler ones may often be unintentionally solved.

[1].A. Gupta, Y. Ong and L. Feng, "Multifactorial Evolution: Toward Evolutionary Multitasking," in IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 343-357, June 2016, doi: 10.1109/TEVC.2015.2458037.