  In many practical optimization problems, the derivatives of the functions to
be optimized are unavailable or unreliable. Such optimization problems are
solved using derivative-free optimization techniques. One of the
state-of-the-art techniques for derivative-free optimization is the covariance
matrix adaptation evolution strategy (CMA-ES) algorithm. However, the
complexity of CMA-ES algorithm makes it undesirable for tasks where fast
optimization is needed. To reduce the execution time of CMA-ES, a parallel
implementation is proposed, and its performance is analyzed using the benchmark
problems in PythOPT optimization environment.
