Parallel Implementation of Genetic Programming on Clusters

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

@InProceedings{tanev:2000:piGPc,
  author =       "Ivan T. Tanev and Takashi Uozumi and Koichi Ono",
  title =        "Parallel Implementation of Genetic Programming on
                 Clusters",
  booktitle =    "Late Breaking Papers at the GECCO'2000 Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  pages =        "388--396",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We present an approach for developing parallel
                 distributed implementation of genetic programming
                 (PDIGP) based on exploitation of the inherent
                 parallelism among semi-isolated subpopulations.
                 Proposed implementation runs on cost-efficient
                 configurations of clusters in LAN and/or Internet
                 environment. PDIGP features single global migration
                 broker and centralized manager of the semi-isolated
                 subpopulations, which contribute to achieving quick
                 propagation of the globally fittest individuals among
                 the subpopulations, reducing the performance demands to
                 the communication network, and achieving flexibility of
                 system configurations by introducing dynamically
                 scaling up opportunities. PDIGP exploits distributed
                 component object model (DCOM) as a communication
                 paradigm, which offers generic support for the issues
                 of naming, locating and protecting the distributed
                 entities in PDIGP. Experimentally obtained results show
                 that in some system configurations the computational
                 effort is less than the computational effort in
                 canonical panmictic GP. Analytically obtained and
                 empirically proved results of the speedup of the
                 computational performance indicate that PDIGP features
                 linear, close to ideal characteristics, which, together
                 with the observed reduction of the computational effort
                 contribute to the acquaintance of hyper-linear overall
                 speedup in developed PDIGP.",
  notes =        "symbolic regression, many node distributed
                 computing

                 Muroran Institute of Technology Mizumoto 27-1,
                 Muroran,JAPAN 050-8585",
}

Genetic Programming entries for Ivan T Tanev Takashi Uozumi Koichi Ono

Citations