Genetic algorithm–Monte Carlo hybrid geometry optimization method for atomic clusters

In this work, an evolutionary type global optimization method for identifying the stable geometries of atomic clusters is developed and applied to carbon clusters for testing purpose. Monte Carlo (MC) type local optimization is used between genetic algorithm (GA) steps together with a special mutation operation designed for the cluster geometry optimization problem. Cluster geometries and the corresponding potential energies for carbon obtained with this GA–MC hybrid method are compared with available results in the literature and reliability of the method is justified for moderate sized carbon clusters.