Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms
| Author | : | |
| Rating | : | 4.59 (518 Votes) |
| Asin | : | 1288228384 |
| Format Type | : | paperback |
| Number of Pages | : | 214 Pages |
| Publish Date | : | 2017-06-15 |
| Language | : | English |
DESCRIPTION:
The LLGA is integrated with the previously developed and tested AFIT CHARMm energy model software. This is an engineering investigation into the effectiveness and efficiency of the Linkage Learning GA (LLGA) applied to the PSP problem. The use of GAs plays an important part in the search for near optimal solutions in large search spaces. Genetic algorithms (GAs) are stochastic search routines that are capable of providing solutions to intractable problems. This new implementation is called the constrained-parallel LLGA (cpLLGA).. The tertiary structure determines the protein's functionality. Ramachandran developed constraints are incorporated into the LLGA to exploit domain knowledge in order to improve the effectiveness of the search technique. This approach, constrained-LLGA (cLLGA), has been parallelized using the same decomposition as the pLLGA. This makes the GA an ideal candidate for finding solutions to the PSP problem. Furthermore, a parallel version, pLLGA, is developed using a data partitioning scheme to "farm out" the CHARMm evaluations. The LLGA implementations takes explicit advantage of "tight linkages" early enough in its algorithmic processing to overcome the disruptive effects of crossover. This model improves the efficiency of the LLGA algorithm. The PSP solution landscape is so large and complex that deterministic
Author review Tanya Deerman My husband actually wrote this thesis It took him the better part of a year. I hope u enjoy it and find it useful