trajectory will create a local optimum that could be worse than finding the two speed parameters that will avoid an acceleration phase that is not undertaken in reality. Even if we did not prove that the objective function is multimodal, we can think that it can possibly be the case. Finally, the objective is non-differentiable (or at least the analytical derivative is out of reach). Hence general-purpose derivative-free optimization method is required.

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [@hansenTutorial2005] is today a state-of-the-art derivative-free optimization method that has demonstrated outstanding performances for problems up to a few hundred variables, in several official comparisons (see, among others, the CEC 2005 challenge [@continuousCEC05], and both Black Box Optimization Benchmark workshops at ACM-GECCO 2009 [@BBOB09] and 2010 [@BBOB10]), as well as on a large number of real-world applications [@CMA-ESApplications]. CMA-ES is an Evolution Strategy [@Rechenberg; @Schwefel] that uses Gaussian mutation with adaptive parameter setting. A Gaussian mutation is defined here by its step-size and its covariance matrix. The step-size is increased (resp. decreased) if the cumulated path of the current best solution is smaller (resp. larger) than that of a random walk, and in the original version [@Hansen:ICEC96; @Hansen:ECJ01], the covariance matrix 