This study describes an alternative method for deconvolution of overlapping characteristic Gauss peaks with the help of optimization of a mixed variable genetic algorithm. Continuous and discrete variables and nonlinear discrete variables in optimization problems cause solution complexity. The processing and analysis of complex analytical signals is important not only in analytical chemistry but also in other fields of science. As the amount of data increases and linearity decreases, high‐performance computations are needed to solve analytical signals. It takes a long time to perform these calculations with traditional processor systems and algorithms. We have used NVIDIA graphical processing units (GPUs) to shorten the duration of these calculations. Solving such analytical signals with genetic algorithms is widely used in computational sciences. In this study, we present a new curve‐fitting method using a genetic algorithm based on Gauss functions used to deconvolve overlapping peaks and find the exact peak number in absorption spectroscopy. The deconvolution of individual bands in the UV‐VIS region is a complex task, because the absorption bands are broad and often strongly overlap. Useful information about the molecular structure and environment can only be obtained by appropriate and truthful separation of these peaks.

中文翻译：

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基于进化计算的混合实数和离散整数优化的高斯峰去卷积

本研究描述了一种在混合变量遗传算法优化的帮助下对重叠特征高斯峰进行解卷积的替代方法。优化问题中的连续和离散变量以及非线性离散变量会导致解决方案的复杂性。复杂分析信号的处理和分析不仅在分析化学中而且在其他科学领域中都很重要。随着数据量的增加和线性度的降低，需要高性能计算来解决分析信号。使用传统的处理器系统和算法执行这些计算需要很长时间。我们使用了 NVIDIA 图形处理单元 (GPU) 来缩短这些计算的持续时间。使用遗传算法解决此类分析信号被广泛用于计算科学。在这项研究中，我们提出了一种新的曲线拟合方法，该方法使用基于高斯函数的遗传算法，用于对重叠峰进行去卷积并在吸收光谱中找到准确的峰数。UV-VIS 区域中单个波段的解卷积是一项复杂的任务，因为吸收波段很宽并且经常强烈重叠。只有通过适当和真实地分离这些峰，才能获得有关分子结构和环境的有用信息。