This paper investigates how genetic algorithms (GAs) can be improved to solve large-scale and complex problems more efficiently. First of all, we review premature convergence, one of the challenges confronted with when applying GAs to real-world problems. Next, some of the methods now available to prevent premature convergence and their intrinsic defects are discussed. A qualitative analysis is then done on the cause of premature convergence that is the loss of building blocks hosted in less-fit individuals during the course of evolution. Thus, we propose a new improver - GAs with Reserve Selection (GARS), where a reserved area is set up to save potential building blocks and a selection mechanism based on individual uniqueness is employed to activate the potentials. Finally, case studies are done in a few standard problems well known in the literature, where the experimental results demonstrate the effectiveness and robustness of GARS in suppressing premature convergence, and also an enhancement is found in global optimization capacity.