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Title: Half a dozen real-world applications of evolutionary multitasking, and more
Authors: Gupta, Abhishek
Zhou, Lei
Ong, Yew-Soon
Chen, Zefeng
Hou, Yaqing
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Gupta, A., Zhou, L., Ong, Y., Chen, Z. & Hou, Y. (2022). Half a dozen real-world applications of evolutionary multitasking, and more. IEEE Computational Intelligence Magazine, 17(2), 49-66.
Project: C211118016
Journal: IEEE Computational Intelligence Magazine
Abstract: Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population's implicit parallelism to jointly solve a set of tasks, hence creating avenues for skills transfer between them. Despite it being early days, the idea of EMT has begun to show promise in a range of real-world applications. In the backdrop of recent advances, the contribution of this paper is twofold. First, a review of several application-oriented explorations of EMT in the literature is presented; the works are assimilated into half a dozen broad categories according to their respective application domains. Each of these six categories elaborates fundamental motivations to multitask, and contains a representative experimental study (referred from the literature). Second, a set of recipes is provided showing how problem formulations of general interest, those that cut across different disciplines, could be transformed in the new light of EMT. Our discussions emphasize the many practical use-cases of EMT, and are intended to spark future research towards crafting novel algorithms for real-world deployment.
ISSN: 1556-603X
DOI: 10.1109/MCI.2022.3155332
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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