Pairwise Sequence Comparison and the Genetic Programming of Iterative Concurrent Programs

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

@InProceedings{ross:1998:pscGPicp,
  author =       "Brian J. Ross",
  title =        "Pairwise Sequence Comparison and the Genetic
                 Programming of Iterative Concurrent Programs",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and 
                 Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max H. Garzon and 
                 David E. Goldberg and Hitoshi Iba and Rick Riolo",
  pages =        "338--343",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.cosc.brocku.ca/~bross/research/gp98.ps",
  URL =          "http://citeseer.ist.psu.edu/503596.html",
  size =         "6 pages",
  abstract =     "The genetic programming of iterative concurrent
                 programs written in the CCS process algebra is
                 investigated. Using a generational genetic programming
                 scheme, experiments succesfully evolved a cyclic
                 concurrent program that performs a even-parity-2
                 analysis on a communicating input stream. The fitness
                 evaluation strategy determines how well programs
                 communicate with randomly generated streams of input
                 signals. Fitness is measured by performing a pairwise
                 sequence alignment comparison of two execution
                 sequences -- the output sequence generated by the
                 program communicating with the test signal stream, and
                 the correct output sequence for that test case. The
                 optimal edit distance between these sequences is
                 efficiently computed using dynamic programming. The
                 main result is that sequence alignment evaluation
                 against randomly generated test cases is a promising
                 evaluation strategy for evolving cycling protocols.",
  notes =        "Quintus Prolog 3.2. GP-98",
}

Genetic Programming entries for Brian J Ross

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