  Many optimization tasks have to be handled in noisy environments, where we
cannot obtain the exact evaluation of a solution but only a noisy one. For
noisy optimization tasks, evolutionary algorithms (EAs), a kind of stochastic
metaheuristic search algorithm, have been widely and successfully applied.
Previous work mainly focuses on empirical studying and designing EAs for noisy
optimization, while, the theoretical counterpart has been little investigated.
In this paper, we investigate a largely ignored question, i.e., whether an
optimization problem will always become harder for EAs in a noisy environment.
We prove that the answer is negative, with respect to the measurement of the
expected running time. The result implies that, for optimization tasks that
have already been quite hard to solve, the noise may not have a negative
effect, and the easier a task the more negatively affected by the noise. On a
representative problem where the noise has a strong negative effect, we examine
two commonly employed mechanisms in EAs dealing with noise, the re-evaluation
and the threshold selection strategies. The analysis discloses that the two
strategies, however, both are not effective, i.e., they do not make the EA more
noise tolerant. We then find that a small modification of the threshold
selection allows it to be proven as an effective strategy for dealing with the
noise in the problem.
