Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms
Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms
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Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms by Karl R Deerman
The tertiary structure determines the protein's functionality. Genetic algorithms (GAs) are stochastic search routines that are capable of providing solutions to intractable problems. The use of GAs plays an important part in the search for near optimal solutions in large search spaces. The PSP solution landscape is so large and complex that deterministic methods flounder due to the combinatoric issues involved with enumerating these massive search spaces. This makes the GA an ideal candidate for finding solutions to the PSP problem. This is an engineering investigation into the effectiveness and efficiency of the Linkage Learning GA (LGA) applied to the PSP problem. The LGA implementations takes explicit advantage of tight linkages early enough in its algorithmic processing to overcome the disruptive effects of crossover. The LGA is integrated with the previously developed and tested AFIT CHARMm energy model software. Furthermore, a parallel version, pLGA, is developed using a data partitioning scheme to farm out the CHARMm evaluations. Portability across AFIT's heterogeneous ABC Beowulf system, distributed networks, and massively parallel platforms is accomplished through the use of object-oriented C++ and the Message Passing Interface (MPI). This model improves the efficiency of the LGA algorithm. Ramachandran developed constraints are incorporated into the LGA to exploit domain knowledge in order to improve the effectiveness of the search technique. This approach, constrained-LGA (cLGA), has been parallelized using the same decomposition as the pLGA. This new implementation is called the constrained-parallel LGA (cpLGA).| SKU | Nicht verfügbar |
| ISBN 13 | 9781288228386 |
| ISBN 10 | 1288228384 |
| Titel | Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms |
| Autor | Karl R Deerman |
| Buchzustand | Nicht verfügbar |
| Bindungsart | Paperback |
| Verlag | Biblioscholar |
| Erscheinungsjahr | 2012-10-29 |
| Seitenanzahl | 214 |
| Hinweis auf dem Einband | Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden. |
| Hinweis | Nicht verfügbar |