Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoretical analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better.