Designing robust volunteer-based evolutionary algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Laredo:2014:GPEM,
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author = "J. L. J. Laredo and P. Bouvry and D. L. Gonzalez and
F. {Fernandez de Vega} and M. G. Arenas and
J. J. Merelo and C. M. Fernandes",
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title = "Designing robust volunteer-based evolutionary
algorithms",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2014",
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volume = "15",
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number = "3",
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pages = "221--244",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Distributed algorithms, Fault tolerance,
Volunteer computing, Peer-to-peer, Desktop grid",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/cKMcx",
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DOI = "doi:10.1007/s10710-014-9213-5",
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size = "24 pages",
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abstract = "This paper tackles the design of scalable and
fault-tolerant evolutionary algorithms computed on
volunteer platforms. These platforms aggregate
computational resources from contributors all around
the world. Given that resources may join the system
only for a limited period of time, the challenge of a
volunteer-based evolutionary algorithm is to take
advantage of a large amount of computational power that
in turn is volatile. The paper analyses first the speed
of convergence of massively parallel evolutionary
algorithms. Then, it provides some guidance about how
to design efficient policies to overcome the
algorithmic loss of quality when the system undergoes
high rates of transient failures, i.e. computers fail
only for a limited period of time and then become
available again. In order to provide empirical
evidence, experiments were conducted for two well-known
problems which require large population sizes to be
solved, the first based on a genetic algorithm and the
second on genetic programming. Results show that, in
general, evolutionary algorithms undergo a graceful
degradation under the stress of losing computing nodes.
Additionally, new available nodes can also contribute
to improving the search process. Despite losing up to
90percent of the initial computing resources,
volunteer-based evolutionary algorithms can find the
same solutions in a failure-prone as in a failure-free
run.",
- }
Genetic Programming entries for
Juan L J Laredo
Pascal Bouvry
Daniel Lombrana Gonzalez Rodriguez
Francisco Fernandez de Vega
Maribel Garcia Arenas
Juan Julian Merelo
Carlos Miguel da Costa Fernandes
Citations