This paper identifies the sequential behavior of the linkage learning genetic algorithm (LLGA), introduces the tightness time model for a single building block, and develops the connection between sequential behavior and the tightness time model. By integrating the first building-block model based on sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is then constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by LLGA when solving uniformly scaled problems.