  The OneMax problem is a standard benchmark optimisation problem for a binary
search space. Recent work on applying a Bandit-Based Random Mutation
Hill-Climbing algorithm to the noisy OneMax Problem showed that it is important
to choose a good value for the resampling number to make a careful trade off
between taking more samples in order to reduce noise, and taking fewer samples
to reduce the total computational cost. This paper extends that observation, by
deriving an analytical expression for the running time of the RMHC algorithm
with resampling applied to the noisy OneMax problem, and showing both
theoretically and empirically that the optimal resampling number increases with
the number of dimensions in the search space.
