Abstract
Conventional genetic algorithms suffer from a dependence on the initial generation used by the algorithm. In case the generation consists 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 to 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. Our computational experience demonstrates the superiority of our method over conventional genetic algorithms.
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