The application of the stochastic genetic algorithm (GA) in conjunction with the deterministic Powell search to analysis of the multicomponent powder EPR spectra based on computer simulation is described. This approach allows for automated extraction of the magnetic parameters and relative abundances of the component signals, from the nonlinear least-squares fitting of experimental spectra, with minimum outside intervention. The efficiency and robustness of GA alone and its hybrid variant with the Powell method was demonstrated using complex simulated and real EPR data sets. The unique capacity of the genetic algorithm for locating global minima, subsequently refined by the Powell method, allowed for successful fitting of the spectra. The influence of the population size, mutation, and crossover rates on the performance of GA was also investigated.

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The application of the stochastic genetic algorithm in tandem with the deterministic Powell method to automated extraction of the magnetic parameters from powder EPR spectra was described. The efficiency and robustness of such hybrid approach were investigated as a function of the uncertainty range of the parameters, using simulated data sets. The discussed results demonstrate superior performance of the hybrid genetic algorithm in fitting of complex spectra in comparison to the common Monte Carlo method joint with the Powell refinement.