Scalable atomistic simulation algorithms for materials research
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An Enabling Framework for Master-Worker Applications on the Computational Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Optimized Evolutionary Strategies in Conformational Sampling
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An enabling framework for parallel optimization on the computational grid
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Local vs. global search strategies in evolutionary GRID-based conformational sampling & docking
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Comparison of parallel multi-objective approaches to protein structure prediction
The Journal of Supercomputing
Hierarchical branch and bound algorithm for computational grids
Future Generation Computer Systems
High performance parallel evolutionary algorithm model based on MapReduce framework
International Journal of Computer Applications in Technology
Population-based harmony search using GPU applied to protein structure prediction
International Journal of Computational Science and Engineering
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Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the problem using the computational grid. The use of a near-optimal metaheuristic, such as a GA, allows a significant reduction in the number of explored potential structures. However, the complexity of the 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. A conjugated gradient-based Hill Climbing local search is combined with the GA in order to intensify the search in the neighborhood of its provided configurations. In this paper we consider two molecular complexes: the tryptophan-cage protein (Brookhaven Protein Data Bank ID 1L2Y) and @a-cyclodextrin. The experimentation results obtained on a computational grid show the effectiveness of the approach.