Using real-valued multi-objective genetic algorithms to model molecular absorption spectra and Raman excitation profiles in solution†
Abstract
The empirical modeling of the absorption spectra and resonance Raman excitation profiles of a large molecule in solution requires adjustment of a minimum of dozens of parameters to fit several hundred data points. This is a difficult optimization problem because all of the observables depend on all of the parameters in a highly coupled and nonlinear manner. Standard nonlinear least-squares fitting methods are highly susceptible to becoming trapped in local minima in the error function unless very good initial guesses for the molecular parameters are made. Here, we demonstrate a method that employs a real-valued genetic algorithm to force a broad search through parameter space to determine the best-fit parameters. The multiobjective genetic algorithm is successful at inverting