Deconvolution of Gaussian peaks with mixed real and discrete‐integer optimization based on evolutionary computing

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. An alternative method of deconvolution is presented with the help of the optimization of the mixed variable genetic algorithm of the overlapping Gauss peaks. The entire problem was designed as a system containing continuous and discrete variables and the solution of this system was tried to be solved using the GPU.