Application of the genetic algorithm (GA) in conjunction with the concept of virtual components (VC) to determine 1D concentration profiles from EPRI spectra (images) is described. In this approach the concentration profile is expressed as the superposition of virtual components described by analytical functions of the Gaussian and Boltzmann type. The method was implemented in the computer program ACon, which allows for fully automated profile extraction via the nonlinear least-squares fitting of experimental images. The parametric sensitivity of the GA internal parameters such as population size, probabilities of the crossover, mutation and elitist retention to the search space was investigated in detail in order to find their optimal settings. The customized genetic algorithm was evaluated using simulated and experimental test data sets and its performance was compared with the Monte Carlo approach.
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