P-CAGE: An Environment for Evolutionary Computation in Peer-to-Peer Systems

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

@InProceedings{eurogp06:FolinoSpezzano,
  author =       "Gianluigi Folino and Giandomenico Spezzano",
  title =        "{P-CAGE:} An Environment for Evolutionary Computation
                 in Peer-to-Peer Systems",
  editor =       "Pierre Collet and Marco Tomassini and Marc Ebner and 
                 Steven Gustafson and Anik\'o Ek\'art",
  booktitle =    "Proceedings of the 9th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3905",
  year =         "2006",
  address =      "Budapest, Hungary",
  month =        "10 - 12 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-33143-3",
  pages =        "341--350",
  DOI =          "doi:10.1007/11729976_31",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Solving complex real-world problems using evolutionary
                 computation is a CPU time-consuming task that requires
                 a large amount of computational resources. Peer-to-Peer
                 (P2P) computing has recently revealed as a powerful way
                 to harness these resources and efficiently deal with
                 such problems. In this paper, we present a P2P
                 implementation of Genetic Programming based on the JXTA
                 technology. To run genetic programs we use a
                 distributed environment based on a hybrid multi-island
                 model that combines the island model with the cellular
                 model. Each island adopts a cellular genetic
                 programming model and the migration occurs among
                 neighbouring peers. The implementation is based on a
                 virtual ring topology. Three different termination
                 criteria (effort, time and max-gen) have been
                 implemented. Experiments on some popular benchmarks
                 show that the approach presents a accuracy at least
                 comparable with classical distributed models, retaining
                 the obvious advantages in terms of decentralisation,
                 fault tolerance and scalability of P2P systems.",
  notes =        "Part of \cite{collet:2006:GP} EuroGP'2006 held in
                 conjunction with EvoCOP2006 and EvoWorkshops2006",
}

Genetic Programming entries for Gianluigi Folino Giandomenico Spezzano

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