FDA (the Factorized Distribution Algorithm) is an evolutionary algorithm that combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. It is then used to generate new points for the next generation. FDA uses a factorization to be able to compute the distribution in polynomial time. Previously, we have shown a convergence theorem for FDA. But it is only valid using Boltzmann selection. Boltzmann selection was not used in practice because a good annealing schedule was lacking. Using a Taylor expansion of the average fitness of the Boltzmann distribution, we have developed an adaptive annealing schedule called SDS. The inverse temperature is changed inversely proportional to the standard deviation. In this work, we compare the resulting scheme to truncation selection both theoretically and experimentally with a series of test functions. We find that it behaves similar in terms of complexity, robustness and efficiency.