Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters

An improved genetic algorithm (GA) is described that has been developed to increase the efficiency of finding the global minimum energy isomers for nanoalloy clusters. The GA is optimized for the example Pt12Pd12, with specific investigation of: the effect of biasing the initial population by seeding; the effect of removing specified clusters from the population (“predation”); and the effect of varying the type of mutation operator applied. These changes are found to significantly enhance the efficiency of the GA, which is subsequently demonstrated by the application of the best strategy to a new cluster, namely Pt19Pd19. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1069–1078, 2005

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