For genetic algorithms, the population may get stuck in a local optimum. The population can escape from this after a long duration. This phenomenon is called punctuated equilibrium. The punctuated equilibria observed in nature and computational ecosystems are known to be well described by diffusion equations. In this paper, simple genetic algorithms are theoretically analyzed to show that they can also be described by a diffusion equation. When fitness is the function of unitation, this analysis can be further refined to make the parameters of genetic algorithms appear in this equation. Using theoretical results on the diffusion equation, the duration of equilibrium is shown to be exponential of such parameters as population size, 1/(mutation probability), and potential barrier. This is corroborated by simulation results for bistable potential landscapes with one local optimum and one global optimum.