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 thereby

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... eating more biased search. The mapping used was non-uniform in nature and was the function of the position of the individual w.r.t. the best solution obtained so far in the algorithm, and hence the name Non-Uniform RGA or in short NRGA. Tests were performed on standard benchmark problems. The results were promising and should encourage further research in this dimension.