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.

中文翻译：

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进化多任务稀疏重建：框架和案例研究

现实世界的应用程序通常有多个要优化的稀疏重建任务。为了利用不同任务之间相似的稀疏模式，本文建立了一个进化多任务框架，使用单个种群同时优化多个稀疏重建任务。在所提出的方法中，进化算法旨在搜索非零分量或行的位置，而不是直接搜索稀疏向量或矩阵。然后使用任务内和任务间遗传转移算子来加强属于相同或不同任务的遗传物质的交换。由于决策向量的长度与测量向量的数量无关，所提出的方法可以有效地解决多个测量向量问题。最后，在进化的多任务环境中研究了高光谱图像分离的案例研究。将均匀区域中的稀疏分解问题视为一项任务是很自然的。信号重建和高光谱图像分离的实验证明了所提出的多任务处理框架对稀疏重建的有效性。