Abstract
The concept of a genetic algorithm (GA) has been applied to the problem of energy minimization in a binary alloy computational cell used in molecular dynamics. The GA has been developed to assign chemical species to each atom of the cell so as to minimize the total energy of the sample while preserving a specified overall chemical composition. Preliminary tests show that the GA may be more effective than traditional Monte Carlo techniques at locating minimum energy configurations. The GA's effectiveness is shown to be related to its ability to “mate” various trial solutions together to form new solutions which perform better than either parent.
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