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Genetic algorithms (GAs) are well-known optimization strategies able to deal with nonlinear functions as those arising in inverse scattering problems. However, they are computationally expensive, thus offering poor performances in terms of general efficiency when compared with inversion techniques based on deterministic optimization methods. In this paper, a parallel implementation of an inverse scattering procedure based on a suitable hybrid genetic algorithm is presented. The proposed strategy is aimed at reducing the overall clock time in order to make the approach competitive with gradient-based methods in terms of runtime, but preserving the capabilities of escaping from local minima. This result is achieved by exploiting the natural parallelism of evolutionary techniques and the searching capabilities of the hybrid approach . The effectiveness of the proposed implementation is demonstrated by considering a selected numerical benchmark related to two-dimensional scattering geometries.