Decomposition of Spectral Band into Gaussian Contours Using an Improved Modification of the Gender Genetic Algorithm

Abstract

One of the methods for the analysis of complex spectral bands (especially for spectra of liquid objects) is their decomposition into a limited number of spectral curves with physically reasonable shapes (Gaussian, Lorentzian, Voigt, etc.). Subsequent analysis of the dependences of the parameters of these contours on some external conditions in which the spectra are obtained may reveal some regularities that bear information about the physical processes taking place in the object. The problem with the required decomposition is that such a decomposition in the presence of noise in spectra is an incorrect inverse problem. Therefore, this problem is often solved by advanced optimization methods that are less likely to become stuck in local minima, such as genetic algorithms (GA). In the conventional version of GA, all individuals are similar regarding the probabilities and implementation of the main genetic operators (crossover and mutation) and the procedure of selection. In their preceding studies, the authors tested the gender GA (GGA), where the individuals of the two genders differ in terms of the mutation probability (higher for males) and the selection procedures for crossover (with the number of crossovers limited for females). In this study, we introduce additional differences between the genders in the procedures of selection and mutation. The improved modification of GGA is tested by comparing the efficiency of the conventional GA, GGA, and three versions of GGA with and without subsequent gradient descent in solving the problems of decomposition of the Raman valence band of liquid water into Gaussian contours.