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Static state estimation is a fundamental tool for control and monitoring of electrical power systems. Commonly used solution methods such as least squares may be faulty when gross errors are present among the measurements and therefore suitable techniques have been developed to detect and identify bad data. In this work, a robust state-estimation procedure is presented. Similarly to the least median of the squares method, the proposed procedure is based on the idea of evaluating the different state vectors obtained by solving samples of the measurements of dimension equal to the number of the states. The remaining measurements agree or disagree with the solution according to the values of their residuals. The optimal solution is the one with the maximum agreement between the remaining measurements and those in the sample. A genetic algorithm is used instead of a random selection of the samples, to speed up the appearance of the optimal solution. The proposed procedure was implemented and tested with reference to some IEEE test systems.