| The performance of Minima Hopping and Evolutionary Algorithms for cluster structure prediction (2008) | |||||||||
Abstract | |||||||||
| We compare Evolutionary Algorithms with Minima Hopping for global optimization in the field of cluster structure prediction. We introduce a new {\em average offspring} recombination operator and compare it with previously used operators. Minima Hopping is improved with a {\em softening} method and a stronger feedback mechanism. Test systems are atomic clusters with Lennard-Jones interaction as well as silicon and gold clusters described by force fields. The improved Minima Hopping is found to be well-suited to all these homoatomic problems. The evolutionary algorithm is more efficient for systems with compact and symmetric ground states, including LJ$_{150}$, but it fails for systems with very complex energy landscapes and asymmetric ground states, such as LJ$_{75}$ and silicon clusters with more than 30 atoms. Both successes and failures of the evolutionary algorithm suggest ways for its improvement. | |||||||||
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