Conventional genetic algorithms suffer from a dependence on the initial generation used by the algorithm. In case the generation cosnsists of solutions which are not close enough to a global optimum but some of which are close to a relatively good local optimum, the algorithm is often guided a converge to the local optimum. In this paper, we provide a method which allows a genetic algorithm to search the solution space more effectively, and increases its chance to attain a global optimum. We provide computational experience with real-valued genetic algorithms on functions of two variables.