Abstract: To overcome the shortcomings of complex computation and easily falling into local optimums for the existing approaches to compute crossover probability,a novel method based on information entropy is presented.The entropy of the population shows the individuals’ evolving potential,and the variance of the population reflects the whole population distribution.The role of the crossover operator in the procedure of population evolving through the entropy of the population and the variance of the population is analyzed.Then,a new computing method is designed for crossover probability so as to get the better control of the evolving process of genetic algorithms.Numerical examples show the method of computing crossover probability can not only be used conveniently but also enhance the robustness,global optimization ability and the convergence speed of the genetic algorithm.The genetic algorithm with the new crossover probability can escape local optimums easily.