document: Application of artificial intelligence to search ground-state geometry of clusters We introduce a global optimization procedure , the neural-assisted genetic algorithm -LRB- NAGA -RRB- . It combines the power of an artificial neural network -LRB- ANN -RRB- with the versatility of the genetic algorithm . This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space . If a reasonable amount of data is available , the ANN can `` understand '' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution . We tested the method in a search for the ground-state geometry of silicon clusters . We trained the ANN with information about the geometry and energetics of small silicon clusters . Next , the ANN learned how to restrict the configurational space for larger silicon clusters . For Si/sub 10 / and Si/sub 20 / , we noticed that the NAGA is at least three times faster than the `` pure '' genetic algorithm . As the size of the cluster increases , it is expected that the gain in terms of time will increase as well
doc_bio_tags: O O B I O O O O O O O O O B I I O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: artificial intelligence global optimization procedure artificial neural network population optimum solution silicon clusters
abstractive_keyphrases: ground-state geometry atomic clusters neural-assisted genetic algorithm si/sub 10/ si/sub 20/ cluster size