  We present our asynchronous implementation of the LM-CMA-ES algorithm, which
is a modern evolution strategy for solving complex large-scale continuous
optimization problems. Our implementation brings the best results when the
number of cores is relatively high and the computational complexity of the
fitness function is also high. The experiments with benchmark functions show
that it is able to overcome its origin on the Sphere function, reaches certain
thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly
performs much better than the original version on the Rastrigin function.
