PoS - Proceedings of Science
Volume 429 - The 6th International Workshop on Deep Learning in Computational Physics (DLCP2022) - Track2. Modern Machine Learning Methods
Decomposition of Spectral Contour into Gaussian Bands using Gender Genetic Algorithm
G. Kupriyanov, I. Isaev, I. Plastinin, T. Dolenko and S. Dolenko*
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Pre-published on: November 15, 2022
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One of the methods for analysis of complex spectral contours (especially for spectra of liquid objects) is their decomposition into a limited number of spectral bands with physically reasonable shapes (Gaussian, Lorentzian, Voigt etc.). The problem with the required decomposition is that such decomposition is an inverse problem that is often ill-conditioned or even incorrect, especially in presence of noise in spectra. Therefore, this problem is often solved by advanced optimization methods less subject to be 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 this study, we test a new version of GA – gender GA (GGA), where the individuals of the two genders differ by the probability of mutation (higher for the male gender) and by the procedures of selection for crossover. In this study, we compare the efficiency of gradient descent and conventional GA and GGA followed by gradient descent from the found point in solving the problems of decomposition of the Raman valence band of liquid water into Gaussian shaped components.
DOI: https://doi.org/10.22323/1.429.0009
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