Designing robust volunteer-based evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  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",
  title =        "Designing robust volunteer-based evolutionary
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2014",
  volume =       "15",
  number =       "3",
  pages =        "221--244",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Distributed algorithms, Fault tolerance,
                 Volunteer computing, Peer-to-peer, Desktop grid",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-014-9213-5",
  size =         "24 pages",
  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

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