In practice, genetic operators have parameters that are fixed before execution, which strongly influence the efficiency and reliability of the algorithm. The optimal choice of these parameters is a multivariate problem in itself and depends on the forms of the operator, the problem to be solved and the characteristics of the fitness function. We studied the effect of several parameters such as population size, crossover probability or mutation rate and selected a set for which independent runs progress steadily towards the lowest energies in a small number of iterations. In what follows, we describe the specificities of the algorithm (Fig. ) and list the corresponding parameters in Section 3.1.