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Genetic Algorithm (GA) is a stochastic search and optimization method imitating the metaphor of natural biological evolution. GA manages population of solutions instead of a single solution to find an optimal solution to a given problem. Although GA draws attention for functional optimization, it may search same point again due to its probabilistic operations that hinder its performance. In this study, we make a novel approach of standard Genetic Algorithm (sGA) to achieve better performance.doi:10.5176/2010-2283_1.2.33 fatcat:cbuw6kfo2rcdrgjlr45cfjih5a