Mapping of Hierarchical Parallel Genetic Algorithms for Protein Folding onto Computational Grids

Weiguo LIU  Bertil SCHMIDT  

IEICE TRANSACTIONS on Information and Systems   Vol.E89-D   No.2   pp.589-596
Publication Date: 2006/02/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.2.589
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Parallel/Distributed Computing and Networking)
Category: Grid Computing
protein folding,  HP lattice models,  hierarchical parallel genetic algorithms,  computational grids,  generic programming,  

Full Text: PDF(606.7KB)>>
Buy this Article

Genetic algorithms are a general problem-solving technique that has been widely used in computational biology. In this paper, we present a framework to map hierarchical parallel genetic algorithms for protein folding problems onto computational grids. By using this framework, the two level communication parts of hierarchical parallel genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the framework to be built in a generic way at application level and thus provides good extensibility and flexibility. Experiments show that it can lead to significant runtime savings on PC clusters and computational grids.