 Only the ranking between candidate solutions is exploited for learning the sample distribution and neither derivatives nor even the function values themselves are required by the method.Two main principles for the adaptation of parameters of the search distribution are exploited in the CMA-ES algorithm.First, a maximum-likelihood principle, based on the idea to increase the probability of successful candidate solutions and search steps.