In order to tackle the problem of “premature” in traditional adaptive genetic algorithms,a new adaptive genetic algorithm based on population diversity is proposed. The key to solve the immature convergence problem is to avoid the loss of population diversity before the algorithm finds the optimal solution. In order to adapt to the changes in population diversity in the evolutionary process, the crossover probability and mutation probability formulas including the variance factor and the population entropy factor are designed. According to the population convergence, the crossover probability and mutation probability are adjusted accordingly to maintain the diversity of the population without destroying the good gene model of the population. By testing standard functions and comparing them with existing algorithms, the results show that the improved algorithm not only can ensure the convergence accuracy, but also increase the convergence speed, and effectively overcome the “premature” problems.