Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms
Language: English
Published by Legare Street Press Okt 2012, 2012
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - 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 (LLGA) applied to the PSP problem. The LLGA implementations takes explicit advantage of 'tight linkages' early enough in its algorithmic processing to overcome the disruptive effects of crossover. The LLGA is integrated with the previously developed and tested AFIT CHARMm energy model software. Furthermore, a parallel version, pLLGA, 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 LLGA algorithm. 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 new implementation is called the constrained-parallel LLGA (cpLLGA).This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.…
Seller Inventory # 9781288228386
- Title
- Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms
- Author
- Karl R. Deerman
- Publisher
- Legare Street Press Okt 2012
- Publication year
- 2012
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1288228384
- ISBN 13
- 9781288228386
- Item weight
- 305 grams
- Dimensions
- 234x156x11 mm
"Synopsis" may belong to another edition of this title.
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