Scalable architecture for parallel distributed implementation of genetic programming on network of workstations

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ivan Tanev and Takashi Uozumi and Koichi Ono",
  title =        "Scalable architecture for parallel distributed
                 implementation of genetic programming on network of
  journal =      "Journal of Systems Architecture",
  volume =       "47",
  pages =        "557--572",
  year =         "2001",
  number =       "7",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Distributed
                 component object model, Island model of parallelism,
                 Network of workstations",
  ISSN =         "1383-7621",
  DOI =          "doi:10.1016/S1383-7621(01)00015-7",
  URL =          "",
  size =         "16 pages",
  abstract =     "We present an approach for developing a scalable
                 architecture for parallel distributed implementation of
                 genetic programming (PDIGP). The approach is based on
                 exploitation of the inherent parallelism among
                 semi-isolated subpopulations in genetic programming
                 (GP). Proposed implementation runs on cost-efficient
                 configurations of networks on workstations in LAN and
                 Internet environment. Developed architecture 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 in system configurations by
                 introducing dynamically scaling up opportunities. PDIGP
                 exploits distributed component object model (DCOM) as a
                 communication paradigm, which as a true system model
                 offers generic support for the issues of naming,
                 locating and protecting the distributed entities in
                 proposed architecture of PDIGP. Experimentally obtained
                 results of computational effort of proposed PDIGP are
                 discussed. The results show that computational effort
                 of PDIGP marginally differs from the computational
                 effort in canonical panmictic GP evolving single large
                 population. For PDIGP running on systems configurations
                 with 16 workstations the computational effort is less
                 than panmictic GP, while for smaller configurations it
                 is insignificantly more. Analytically obtained and
                 empirically proved results of the speedup of
                 computational performance indicate that PDIGP features
                 linear, close to ideal characteristics. Experimentally
                 obtained results of PDIGP running on configurations
                 with eight workstations show close to 8-fold overall
                 speedup. These results are consistent with the
                 anticipated cumulative effect of the insignificant
                 increase of computational effort for the considered
                 configuration and the close to linear speedup of
                 computational performance.",

Genetic Programming entries for Ivan T Tanev Takashi Uozumi Koichi Ono