A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction

Alexandru-Adrian Tantar 1, 2 Nouredine Melab 2 El-Ghazali Talbi 2, 3
1 GRAND-LARGE - Global parallel and distributed computing
LRI - Laboratoire de Recherche en Informatique, LIFL - Laboratoire d'Informatique Fondamentale de Lille, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : 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.
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Soft Computing, Springer Verlag, 2008, 12, pp.1185-1198. 〈10.1007/s00500-008-0298-8〉
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Soumis le : mercredi 18 avril 2012 - 11:23:22
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

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Alexandru-Adrian Tantar, Nouredine Melab, El-Ghazali Talbi. A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing, Springer Verlag, 2008, 12, pp.1185-1198. 〈10.1007/s00500-008-0298-8〉. 〈hal-00688680〉

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