Characterizing fault tolerance in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Daniel Lombrana Gonzalez and 
                 Francisco {Fernandez de Vega} and Henri Casanova",
  title =        "Characterizing fault tolerance in genetic
  booktitle =    "BADS '09: Proceedings of the 2009 workshop on
                 Bio-inspired algorithms for distributed systems",
  year =         "2009",
  editor =       "Gianluigi Folino and Natalio Krasnogor and 
                 Carlo Mastroianni and Franco Zambonelli",
  pages =        "1--10",
  address =      "Barcelona, Spain",
  publisher_address = "New York, NY, USA",
  month =        jun # " 15-19",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming,
                 Fault-tolerance, parallel genetic programming, desktop
  isbn13 =       "978-1-60558-584-0",
  URL =          "",
  DOI =          "doi:10.1145/1555284.1555286",
  size =         "10 pages",
  abstract =     "Evolutionary Algorithms (EAs), and particularly
                 Genetic Programming (GP), are techniques frequently
                 employed to solve difficult real-life problems, which
                 can require up to days or months of computation. One
                 approach to reduce the time to solution is to use
                 parallel computing on distributed platforms.
                 Distributed platforms are prone to failures, and when
                 these platforms are large and/or low-cost, failures are
                 expected events rather than catastrophic exceptions.
                 Therefore, fault tolerance and recovery techniques
                 often become necessary. It turns out that Parallel GP
                 (PGP) applications have an inherent ability to tolerate
                 failures. This ability is quantified via simulation
                 experiments performed using failure traces from
                 real-world distributed platforms, namely, desktop grids
                 (DGs), for two well-known GP problems. A simple
                 technique is then proposed by which PGP applications
                 can better tolerate the different, and often high,
                 failures rates seen in different platforms.",
  notes =        "Also known as \cite{1555286} even-5-parity,

Genetic Programming entries for Daniel Lombrana Gonzalez Rodriguez Francisco Fernandez de Vega Henri Casanova