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Genetic-algorithm-based reconstruction in diffusion tomography

机译:扩散层析成像中基于遗传算法的重建

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Abstract: A genetic algorithm based approach is employed in the inverse problem of reconstructing the interior of a diffusing medium. Diffusion of optical energy in a scattering medium is simulated by a relaxation scheme. The genetic algorithm uses an error measure to successively modify an initial set of solutions yielding new generations of improved solutions. The error measure, which determines the relative merit of a particular solution, is determined by comparing the data obtained by simulating diffusion through the solution with those for the unknown medium. Unlike conventional iterative schemes, successive generations of solutions are generated through a directed parallel search in the solution space without any knowledge of the derivative of the error surface. The parallel search mechanism alleviates the problem of getting trapped in local minima. Results of experiments performed on two-dimensional planar media are presented along with suggestions for hybrid approaches that incorporate other reconstruction schemes. !26
机译:摘要:在重建扩散介质内部的反问题中,采用了一种基于遗传算法的方法。通过松弛方案模拟光能在散射介质中的扩散。遗传算法使用错误度量来连续修改一组初始解决方案,从而产生新一代的改进解决方案。通过比较模拟溶液中的扩散与未知介质的扩散所获得的数据,可以确定确定特定解决方案相对价值的误差度量。与常规迭代方案不同,在不知道误差面导数的情况下,通过在解决方案空间中进行有向并行搜索来生成解决方案的连续生成。并行搜索机制缓解了陷入局部最小值的问题。提出了在二维平面介质上执行的实验结果以及结合其他重建方案的混合方法的建议。 !26

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