We study the effect of the potential energy function on the global minimum structures of argon clusters arising in the optimization performed by genetic algorithms (GAs). We propose a robust and efficient GA which allows for the calculation of all of the putative global minima of Ar N (N ) 3-78) clusters modeled with four different potentials. Both energetic and structural properties of such minima are compared among each other and with those previously obtained for the Lennard-Jones function. In addition, the possibility of obtaining global minima of one potential through local optimization over the corresponding cluster geometry given by other potentials was associated with some structural parameters. The influence of the contribution from the three-body (Axilrod-Teller-Muto) triple-dipole potential (including or not a damping function to describe its correct behavior at smaller interatomic distances) has also been analyzed.

State of the art algorithms for cluster geometry optimization rely on hybrid approaches that combine the global exploration performed by evolutionary methods with local search procedures. These methods use derivative information to discover the nearest local optimum. In this paper we analyze the locality properties of this approach to gain insight on the algorithm's strengths and weaknesses and to determine the role played by each of its components. Results show that there are important differences in what concerns the locality of different mutation operators commonly used in this problem.

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