Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139440
Title: Evolutionary multitasking sparse reconstruction : framework and case study
Authors: Li, Hao
Ong, Yew-Soon
Gong, Maoguo
Wang, Zhenkun
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Li, H., Ong, Y.-S., Gong, M., & Wang, Z. (2019). Evolutionary multitasking sparse reconstruction : framework and case study. IEEE Transactions on Evolutionary Computation, 23(5), 733-747. doi:10.1109/tevc.2018.2881955
Journal: IEEE Transactions on Evolutionary Computation
Abstract: Real-world applications typically have multiple sparse reconstruction tasks to be optimized. In order to exploit the similar sparsity pattern between different tasks, this paper establishes an evolutionary multitasking framework to simultaneously optimize multiple sparse reconstruction tasks using a single population. In the proposed method, the evolutionary algorithm aims to search the locations of nonzero components or rows instead of searching sparse vector or matrix directly. Then the within-Task and between-Task genetic transfer operators are employed to reinforce the exchange of genetic material belonging to the same or different tasks. The proposed method can solve multiple measurement vector problems efficiently because the length of decision vector is independent of the number of measurement vectors. Finally, a case study on hyperspectral image unmixing is investigated in an evolutionary multitasking setting. It is natural to consider a sparse unmixing problem in a homogeneous region as a task. Experiments on signal reconstruction and hyperspectral image unmixing demonstrate the effectiveness of the proposed multitasking framework for sparse reconstruction.
URI: https://hdl.handle.net/10356/139440
ISSN: 1089-778X
DOI: 10.1109/TEVC.2018.2881955
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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