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Genetic algorithms have been used as optimization tool using evolutionary strategies. Genetic algorithms cover three basic steps for population refinement selection, cross-over and mutation. In normal Real-coded genetic algorithm(RGA), the population of real variables generated after population refinement operations, is used as it is for the computation of the objective function. In this paper we have shown the effect made by mapping the refined population towards better solutions and therebydoi:10.1109/cec.2014.6900621 dblp:conf/cec/DhebarDB14 fatcat:tvnyhuav2nbu3mvwamg346eeo4