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.
%0 Journal Article
%1 Liu:2006:ITIS
%A Liu, Weiguo
%A Schmidt, Bertil
%D 2006
%J IEICE Transactions on Information and Systems
%K HP algorithms, computational folding, generic genetic grids, hierarchical lattice models, parallel programming programming, protein
%N 2
%P 589--596
%R doi:10.1093/ietisy/e89-d.2.589
%T Mapping of Hierarchical Parallel Genetic Algorithms
for Protein Folding onto Computational Grids
%V E89-D
%X 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.
@article{Liu:2006:ITIS,
abstract = {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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Liu, Weiguo and Schmidt, Bertil},
biburl = {https://www.bibsonomy.org/bibtex/253a6ea4a5d00e4901c2e26e91ea8b96f/brazovayeye},
doi = {doi:10.1093/ietisy/e89-d.2.589},
email = {liuweiguo@pmail.ntu.edu.sg},
interhash = {598c198b7bed93512425c0f92d8106d0},
intrahash = {53a6ea4a5d00e4901c2e26e91ea8b96f},
issn = {0916-8532},
journal = {IEICE Transactions on Information and Systems},
keywords = {HP algorithms, computational folding, generic genetic grids, hierarchical lattice models, parallel programming programming, protein},
notes = {Special Section on Parallel/Distributed Computing and
Networking -- Papers -- Grid Computing Copyright 2005
IEICE},
number = 2,
pages = {589--596},
timestamp = {2008-06-19T17:45:42.000+0200},
title = {Mapping of Hierarchical Parallel Genetic Algorithms
for Protein Folding onto Computational Grids},
volume = {E89-D},
year = 2006
}