Global optimization
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
On success rates for controlled random search
Journal of Global Optimization
Heterogeneous computing and parallel genetic algorithms
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Journal of Parallel and Distributed Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Parallel heterogeneous genetic algorithms for continuous optimization
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Future Generation Computer Systems
Guest Editors' Introduction: Scientific Applications of Grid Computing
Computing in Science and Engineering
Concurrency and Computation: Practice & Experience
Guest Editors' Introduction: Scientific Applications of Grid Computing, Part II
Computing in Science and Engineering
The impact of data replication on job scheduling performance in the Data Grid
Future Generation Computer Systems
Benchmarking of high throughput computing applications on Grids
Parallel Computing
Evaluating the reliability of computational grids from the end user's point of view
Journal of Systems Architecture: the EUROMICRO Journal
Electron tomography of complex biological specimens on the Grid
Future Generation Computer Systems
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Journal of Global Optimization
Introducing grid speedup Γ: a scalability metric for parallel applications on the grid
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
An overview of the EGEE project
DELOS'04 Proceedings of the 6th Thematic conference on Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures
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Reaching as high structural resolution as possible in 3D electron microscopy of biological specimens is crucial to understanding their function and interactions. Technical and biological limitations make electron microscopy projections of such specimens quite noisy. Under those circumstances, the broadly used Weighted Back-Projection algorithm presents some limitations for 3D reconstruction. Iterative tomographic reconstruction algorithms are well suited to provide high resolution 3D structures under such noisy conditions. Nevertheless, these iterative algorithms present two major challenges: computational expensiveness and some free parameters which need to be correctly tuned to obtain the best possible resolution. This work applies global optimization techniques to search for the optimal set of parameters and makes use of the high-throughput capabilities of grid computing to perform the required computations. Fault tolerance techniques have been included in our application to deal with the dynamic nature and complexity of large scale computational grids. The approach for parameter optimization presented here has been successfully evaluated in the European EGEE grid, obtaining good levels of speedup, throughput and transfer rates.