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This presentation introduces Geneva, an Open Source library of distributed optimization algorithms that is heavily based on the Boost library collection. Geneva currently covers gradient descents, evolutionary- and swarm algorithms, and will soon also include simulated annealing. All algorithms act on the same data structures, so that candidate solutions can move freely between optimization algorithms. Geneva allows the solution of large-scale parametric optimization problems on multi-core systems and clusters, as well as in Grid- and Cloud-environments.

The library has been tested with several hundred clients, simultaneously working on optimization problems. Among the libraries used are: Boost.Serialization, Threads, Conversion, Date/Time, Function, Bind, and many more. The presentation discusses the Geneva library itself, as well as the experiences made with Boost, from a user's perspective.
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