We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

中文翻译：

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使用逆遗传算法推进X射线散射计量。

我们将两种遗传优化算法的速度和有效性与通过马尔可夫链蒙特卡洛算法进行统计采样的结果进行比较，以发现哪种方法最能确定周期性光栅中使用临界尺寸小角度X射线散射测量的真实空间结构， 。协方差矩阵适应进化策略和差分进化算法均已实现，并使用各种目标函数进行了比较。算法和目标函数用于最小化衍射模拟和测量衍射数据之间的差异。这些模拟使用已知的电子密度模型进行参数化，该模型大致对应于我们纳米光栅的真实空间结构。研究表明，对于X射线散射数据，