Uncheatable Distributed Computations
CT-RSA 2001 Proceedings of the 2001 Conference on Topics in Cryptology: The Cryptographer's Track at RSA
Hardening Functions for Large Scale Distributed Computations
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
High-Performance Task Distribution for Volunteer Computing
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Distributed hyper-heuristics for real parameter optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Review: Volunteer computing: requirements, challenges, and solutions
Journal of Network and Computer Applications
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Computational science is placing new demands on distributed computing systems as the rate of data acquisition is far outpacing the improvements in processor speed. Evolutionary algorithms provide efficient means of optimizing the increasingly complex models required by different scientific projects, which can have very complex search spaces with many local minima. This work describes different validation strategies used by MilkyWay@Home, a volunteer computing project created to address the extreme computational demands of 3-dimensionally modeling the Milky Way galaxy, which currently consists of over 27,000 highly heterogeneous and volatile computing hosts, which provide a combined computing power of over 1.55 petaflops. The validation strategies presented form a foundation for efficiently validating evolutionary algorithms on unreliable or even partially malicious computing systems, and have significantly reduced the time taken to obtain good fits of MilkyWay@Home’s astronomical models.