  We return to the geometry optimization problem of Lennard-Jones clusters to
analyze the performance dependence of "cut and splice" genetic algorithms (GAs)
on the employed population size. We generally find that admixing twinning
mutation moves leads to an improved robustness of the algorithm efficiency with
respect to this a priori unknown technical parameter. The resulting very stable
performance of the corresponding mutation+mating GA implementation over a wide
range of population sizes is an important feature when addressing unknown
systems with computationally involved first-principles based GA sampling.
