Name
Abstract | ||
|---|---|---|
Genetic algorithm was employed to optimize the structures of (C60)N molecular clusters with the lowest energy. Aiming at an effective solution of stable structures, some improvements are made to traditional genetic algorithm. Firstly, gene is coded by the mixed method (real number and integral number) for initialized population quality. Secondly, a new selection mechanism based on roulette wheel and hamming distance is introduced. Thirdly, in order to enhance the chromosome diversity, a new self-adaptive crossover method is developed which combines 1-point crossover with uniform crossover. Finally, for the sake of improving the global searching capacity and avoiding the premature convergence, a feedback mutation based on dynamic encoding is put forward. The experiment results show that the improved genetic algorithm is good at rapidity and convergence as well as can search for the stable structure when N varies from 3 to 25. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISKE.2008.4730978 | Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference |
Keywords | DocType | Volume |
biocomputing,genetic algorithms,molecular clusters,(C60)N molecular clusters,chromosome diversity,genetic algorithm,hamming distance,initialized population quality,roulette wheel,self-adaptive crossover method,stable structures | Conference | 1 |
ISBN | Citations | PageRank |
978-1-4244-2197-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guifang Shao | 1 | 0 | 0.34 |
| Yuhua Wen | 2 | 0 | 0.34 |
| Yaohua Chen | 3 | 0 | 0.34 |