2007 · 35 citations · 9 references
Cluster ComputingEngineeringComputer ArchitectureNetwork ComputingEvolutionary AlgorithmsEvolution StrategyInternet ComputingInternet Of ThingsParallel ComputingEvolution-based MethodEvolutionary Computation ExperimentComputer EngineeringComputer ScienceDistributed ProcessingScalable ComputingEvolutionary ProgrammingDistributed ComputingEdge ComputingEvolutionary BiologyCloud ComputingParallel ProgrammingDistributed Evolutionary Computation
The challenge of ad-hoc computing is to find the way of taking advantage of spare cycles in an efficient way that takes into account all capabilities of the devices and inter connections available to them. In this paper we explore distributed evolutionary computation based on the Ruby on Rails framework, which overlays a Model-View-Controller on evolutionary computation. It allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in an evolutionary computation experiment. Using a straight forward farming model, we consider different factors, such as the size of the population used. We are mostly interested in how they impact on performance, but also the scaling behavior when a non-trivial number of computers is applied to the problem. Experiments show the impact of different packet sizes on performance, as well as a quite limited scaling behavior, due tothe characteristics of the server. Several solutions for that problem are proposed.
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High-Performance Task Distribution for Volunteer Computing
David P. Anderson, Eric Korpela, R WALTON · 2006 · 165 citations
A Note on the Griewank Test Function
Marco Locatelli · Journal of Global Optimization · 2003 · 114 citations