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Genetic algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of computation load and memory. Parallel genetic algorithms (PGAs) are parallel implementations of GAs which can provide considerable gains in terms of performance and scalability. PGAs can easily be implemented on networks of heterogeneous computers or on parallel mainframes. In this paper the author has discussed the concept of PGAs and implementation of master slave paradigm (one of the possible approaches in design of PGAs) using MPI library on a Beowulf Linux Cluster.