Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
SETI@home: an experiment in public-resource computing
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New Generation Computing - Grid systems for life sciences
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E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
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E-SCIENCE '07 Proceedings of the Third IEEE International Conference on e-Science and Grid Computing
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
The effects of heterogeneity on asynchronous panmictic genetic search
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
A probabilistic approach to finding geometric objects in spatial datasets of the milky way
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Querying simplexes in quasi-triangulation
Computer-Aided Design
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EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Improving volunteer computing scheduling for evolutionary algorithms
Future Generation Computer Systems
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This paper examines the use of a probabilistic simplex operator for asynchronous genetic search on the BOINC volunteer computing framework. This algorithm is used to optimize a computationally intensive function with a continuous parameter space: finding the optimal fit of an astronomical model of the Milky Way galaxy to observed stars. The asynchronous search using a BOINC community of over 1,000 users is shown to be comparable to a synchronous continuously updated genetic search on a 1,024 processor partition of an IBM BlueGene/L supercomputer. The probabilistic simplex operator is also shown to be highly effective and the results demonstrate that increasing the parents used to generate offspring improves the convergence rate of the search. Additionally, it is shown that there is potential for improvement by refining the range of the probabilistic operator, adding more parents, and generating offspring differently for volunteered computers based on their typical speed in reporting results. The results provide a compelling argument for the use of asynchronous genetic search and volunteer computing environments, such as BOINC, for computationally intensive optimization problems and, therefore, this work opens up interesting areas of future research into asynchronous optimization methods.