Surrogate model based Evolution Strategies (like the doubly trained surrogate model CMA-ES, DTS-CMA-ES) use a model of the objective function to reduce the number of function evaluations during optimization. This work investigates to use the expected selection weights averaged over the GP posterior distribution as replacement of the fitness and to guide point-selection for evaluation via the variance of the weights. Results obtained on BBOB show that the proposed technique performs on par with current strategies and allows the usage of surrogate models that are invariant to strictly increasing transformations of the function values. However, initial experiments showed that simple modeling of ranks in the GP does lead to worse results than current GP models of the function values.