Robustness of "cut and splice" genetic algorithms in the structural optimization of atomic clusters

Abstract

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.

5 pages including 3 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.html

References in corpus (4)

• Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
• Molecular geometry optimization with a genetic algorithm
• Evolution of the Potential Energy Surface with Size for Lennard-Jones Clusters
• The performance of Minima Hopping and Evolutionary Algorithms for cluster structure prediction