Premium
Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters
Author(s) -
Lloyd Lesley D.,
Johnston Roy L.,
Salhi Said
Publication year - 2005
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20247
Subject(s) - genetic algorithm , cluster (spacecraft) , algorithm , population , operator (biology) , computer science , mathematical optimization , seeding , mutation , materials science , mathematics , chemistry , physics , gene , thermodynamics , biochemistry , demography , repressor , sociology , transcription factor , programming language
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 Pt 12 Pd 12 , 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 Pt 19 Pd 19 . © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1069–1078, 2005