Avoiding the Bloat with Stochastic Grammar-based Genetic Programming

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  author =       "Alain Ratle and Michele Sebag",
  title =        "Avoiding the Bloat with Stochastic Grammar-based
                 Genetic Programming",
  journal =      "CoRR",
  year =         "2006",
  volume =       "abs/cs/0602022",
  month =        "7 " # feb,
  howpublished = "arxiv",
  note =         "arXiv",
  keywords =     "genetic algorithms, genetic programming, Artificial
  URL =          "http://arxiv.org/abs/cs/0602022",
  size =         "13 pages",
  abstract =     "The application of Genetic Programming to the
                 discovery of empirical laws is often impaired by the
                 huge size of the search space, and consequently by the
                 computer resources needed. In many cases, the extreme
                 demand for memory and CPU is due to the massive growth
                 of non-coding segments, the introns. The paper presents
                 a new program evolution framework which combines
                 distribution-based evolution in the PBIL spirit, with
                 grammar-based genetic programming; the information is
                 stored as a probability distribution on the grammar
                 rules, rather than in a population. Experiments on a
                 real-world like problem show that this approach gives a
                 practical solution to the problem of intron growth.",
  bibdate =      "2008-01-02",
  bibsource =    "DBLP,

Genetic Programming entries for Alain Ratle Michele Sebag