Genetic Algorithm (GA) is a stochastic search andoptimization method imitating the metaphor of naturalbiological evolution. GA manages population of solutionsinstead of a single solution to find an optimal solution to agiven problem. Although GA draws attention for functionaloptimization, it may search same point again due to itsprobabilistic operations that hinder its performance. In thisstudy, we make a novel approach of standard GeneticAlgorithm (sGA) to achieve better performance. Themodification of sGA is investigated in selection andrecombination stages and proposed Precise Genetic Algorithm(PGA). PGA searches the target space efficiently and it showsseveral potential advantages over the conventional GA whentested for solving functions having multiple independentvariables
