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Summary form only given. Evolutionary algorithms (EAs) are applied to solve the radio network design problem (RND). The task is to find the best set of transmitter locations in order to cover a given geographical region at an optimal cost. Usually, parallel EAs are needed in order to cope with the high computational requirements of such a problem. Here, we try to develop and evaluate a set of sequential and parallel genetic algorithms (GAs) in order to solve efficiently the RND problem. The results show that our distributed steady state GA is an efficient and accurate tool for solving RND that even outperforms existing parallel solutions. The sequential algorithm performs very efficiently from a numerical point of view, although the distributed version is much faster, with an observed linear speedup.