Adaptive Migration for the Distributed Genetic Programming

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

@PhdThesis{Paulikas:thesis,
  author =       "Giedrius Paulikas",
  title =        "Adaptive Migration for the Distributed Genetic
                 Programming",
  school =       "Technological science, Informatics Engineering, Kaunas
                 University of Technology",
  year =         "2007",
  address =      "Kaunas, Lithuania",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://en.ktu.lt/sites/default/files/2007-09-07%20giedrius%20paulikas.pdf",
  abstract =     "The work studies a genetic programming algorithm that
                 is used for automatic generation of computer programs.
                 The distributed version of this algorithm processes
                 several large parts of the program population
                 (subpopulations) separately, occasionally moving a
                 small quantity of individuals among the subpopulations.
                 The distributed genetic programming algorithm exhibits
                 a higher search speed when compared to traditional
                 sequential counterpart, but it has additional
                 distribution parameters. The selection of these
                 parameters can be performed automatically during
                 algorithm run time, this also improves the
                 effectiveness of the search. Migration among the
                 subpopulations is controlled using the flocking
                 algorithm. Flocking algorithm is used to move
                 independent agents, where each agent acts according to
                 a set of simple rules that help to keep a constant
                 shape of the flock. The measure of individual location
                 in the search space is required in order to be able to
                 apply flocking rules to the flock of genetic
                 programming subpopulations. Two different origins of
                 location measures are examined, namely the genotype
                 based and phenotype based locations. The latter,
                 phenotype, or fitness, based measure is preferred.
                 Topology of migration is formed by moving programs to
                 the most distant neighbors of the subpopulation. The
                 observation of the progress of algorithm run shows that
                 adapted flocking rules keep the bigger distance among
                 the flock mates and cover the larger portion of search
                 space. When genetic programming with adaptive migration
                 control is experimentally compared to traditional
                 sequential and distributed versions of the algorithm,
                 generally an increase of effectiveness of the search is
                 recorded. The adaptive control also eliminates the
                 necessity of analysis of the problem domain that is
                 required to choose the optimal distribution parameters
                 of the algorithm.",
  notes =        "http://en.ktu.lt/sites/default/files/2007-09-07%20giedrius%20paulikas.pdf
                 gives English language summary",
}

Genetic Programming entries for Giedrius Paulikas

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