A grid-based genetic algorithm combined ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction
Author(s) :
Tantar, Alexandru-Adrian [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Global parallel and distributed computing [GRAND-LARGE]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Global parallel and distributed computing [GRAND-LARGE]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Journal title :
Soft Computing
Pages :
1185-1198
Publisher :
Springer Verlag
Publication date :
2008
ISSN :
1432-7643
HAL domain(s) :
Informatique [cs]/Autre [cs.OH]
English abstract : [en]
A hierarchical hybrid model of parallel metaheuristics is proposed, combining an evolutionary algorithm and an adaptive simulated annealing. The algorithms are executed inside a grid environment with different parallelization ...
Show more >A hierarchical hybrid model of parallel metaheuristics is proposed, combining an evolutionary algorithm and an adaptive simulated annealing. The algorithms are executed inside a grid environment with different parallelization strategies: the synchronous multi-start model, parallel evaluation of different solutions and an insular model with asynchronous migrations. Furthermore, a conjugated gradient local search method is employed at different stages of the exploration process. The algorithms were evaluated using the protein structure prediction problem, having as benchmarks the tryptophan-cage protein (Brookhaven Protein Data Bank ID: 1L2Y), the tryptophan-zipper protein (PDB ID: 1LE1) and the α-Cyclodextrin complex. Experimentations were performed on a nation-wide grid infrastructure, over six distinct administrative domains and gathering nearly 1,000 CPUs. The complexity of the protein structure prediction problem remains prohibitive as far as large proteins are concerned, making the use of parallel computing on the computational grid essential for its efficient resolution.Show less >
Show more >A hierarchical hybrid model of parallel metaheuristics is proposed, combining an evolutionary algorithm and an adaptive simulated annealing. The algorithms are executed inside a grid environment with different parallelization strategies: the synchronous multi-start model, parallel evaluation of different solutions and an insular model with asynchronous migrations. Furthermore, a conjugated gradient local search method is employed at different stages of the exploration process. The algorithms were evaluated using the protein structure prediction problem, having as benchmarks the tryptophan-cage protein (Brookhaven Protein Data Bank ID: 1L2Y), the tryptophan-zipper protein (PDB ID: 1LE1) and the α-Cyclodextrin complex. Experimentations were performed on a nation-wide grid infrastructure, over six distinct administrative domains and gathering nearly 1,000 CPUs. The complexity of the protein structure prediction problem remains prohibitive as far as large proteins are concerned, making the use of parallel computing on the computational grid essential for its efficient resolution.Show less >
Language :
Anglais
Popular science :
Non
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