Abstract
From the era of big science we have back to the ”do it yourself” era of science, where you don’t have any money to buy clusters and subscribe to grids but still have algorithms that cravemany computing nodes and need them for scalability. Fortunately, this coincides with the era of big data, cloud computing, and browsers including JavaScript virtual machines. This talk will concentrate on two different aspects of volunteer or freeriding computing: first, the pragmatic: where to find those resources, which can be used, what kind of support you have to give them; and then, the theoretical: how algorithms can be adapted to a environment in which nodes come and go, have different computing capabilites and operate in complete asynchrony of each other.
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Merelo, J.J. (2015). Low or No Cost Distributed Evolutionary Computation. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_1
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DOI: https://doi.org/10.1007/978-3-319-10422-5_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10421-8
Online ISBN: 978-3-319-10422-5
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