Neural-network-assisted genetic algorithm applied to silicon clusters
- Department of Physics, Instituto Tecnologico de Aeronautica, Pca. Marechal Eduardo Gomes, 50-Sao Jose dos Campos, Sao Paulo 12228-900 (Brazil)
Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Si{sub n} (10{<=}n{<=}15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.
- OSTI ID:
- 20633856
- Journal Information:
- Physical Review. A, Vol. 67, Issue 3; Other Information: DOI: 10.1103/PhysRevA.67.033203; (c) 2003 The American Physical Society; Country of input: International Atomic Energy Agency (IAEA); ISSN 1050-2947
- Country of Publication:
- United States
- Language:
- English
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