One of the most important features of an optimization method is its response to an increase in the number of variables, n . Random stochastic hill climber (SHC) and univariate marginal distribution algorithms (UMDA) are two fundamentally different stochastic optimizers. SHC proceeds with local perturbations while UMDA infers and uses a global probability density. The response to dimensionality of the two methods is compared both numerically and theoretically on unimodal functions. SHC response is , while UMDA response ranges from to . On two test problems whose sizes go up to 7 200 , SHC is faster than UMDA.