A Genetic Programming Ecosystem

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

@InProceedings{devaney:2001:gpe,
  author =       "Judith Devaney and John Hagedorn and 
                 Olivier Nicolas and Gagan Garg and Aurelien Samson and Martial Michel",
  title =        "A Genetic Programming Ecosystem",
  booktitle =    "Proceedings 15th International Parallel and
                 Distributed Processing Symposium, Abstracts and CDROM",
  year =         "2001",
  pages =        "1323--1330",
  address =      "Los Alamitos, CA, USA",
  howpublished = "Abstracts and CD-ROM",
  month =        "23-27 " # apr,
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-0990-8",
  URL =          "http://math.nist.gov/mcsd/savg/papers/bio.pdf",
  URL =          "http://math.nist.gov/mcsd/savg/papers/bio.pp.gz",
  note =         "IPDPS2001:WS",
  abstract =     "Algorithms are needed in every aspect of parallel
                 computing. Genetic Programming is an evolutionary
                 technique for automating the design of algorithms
                 through iterative steps of mutation and crossover
                 operations on an initial population of randomly
                 generated computer programs. This paper describes a
                 novel parallel genetic programming (GP) system inspired
                 by the symbiogenesis model of evolution, wherein new
                 organisms are generated through the absorption of
                 different life-forms in addition to the usual mutation
                 and crossover operations. Different organisms are
                 expressed in this GP system through multiple program
                 representations. Two program representations considered
                 in this paper are the procedural representation (PR)
                 and the tree representation (TR). Populations of these
                 representations evolve separately. Individuals in each
                 population migrate to the other and participate in
                 evolution via representation change algorithms.
                 Parallelism is achieved through use of the
                 AutoMap/AutoLink MPI library. The differences in the
                 locality properties of the representations serve as a
                 source of new ideas for creating the final algorithm.",
}

Genetic Programming entries for Judith E Devaney John G Hagedorn Olivier Nicolas Gagan Garg Aurelien Samson Martial Michel

Citations