Reference : A grid-based genetic algorithm combined with an adaptive simulated annealing for prot...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/10522
A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction
English
Tantar, Alexandru-Adrian mailto [INRIA Lille Nord Europe Res Ctr, F-59650 Villeneuve Dascq, France.]
Melab, Nouredine [INRIA Futurs, DOLPHIN Project, LIFL CNRS UMR 8022, F-59655 Villeneuve Dascq, France.]
Talbi, El-Ghazali [INRIA Futurs, DOLPHIN Project, LIFL CNRS UMR 8022, F-59655 Villeneuve Dascq, France.]
2008
SOFT COMPUTING
Springer
12
12
1185-1198
Yes (verified by ORBilu)
1432-7643
New York
[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 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 alpha-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.
http://hdl.handle.net/10993/10522
10.1007/s00500-008-0298-8

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