Competitively Evolving Decision Trees Against Fixed Training Cases for Natural Language Processing

Created by W.Langdon from gp-bibliography.bib Revision:1.1603

@InCollection{kinnear:siegel,
  title =        "Competitively Evolving Decision Trees Against Fixed
                 Training Cases for Natural Language Processing",
  author =       "Eric V. Siegel",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "19",
  pages =        "409--423",
  size =         "15 pages",
  URL =          "http://www1.cs.columbia.edu/nlp/papers/1994/siegel_94.pdf",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262111888",
  keywords =     "genetic algorithms, genetic programming,
                 co-evolution",
  abstract =     "Competitive fitness functions can generate performance
                 superior to absolute fitness functions [Angeline and
                 Pollack 1993], [Hillis 1992]. This chapter describes a
                 method by which competition can be implemented when
                 training over a fixed (static) set of examples. Since
                 new training cases cannot be generated by mutation or
                 crossover, the probabilistic frequencies by which
                 individual training cases are selected competitively
                 adapt. We evolve decision trees for the problem of word
                 sense disambiguation. The decision trees contain
                 embedded bit strings; bit string crossover is
                 intermingled with subtree-swapping. To approach the
                 problem of overlearning, we have implemented a fitness
                 penalty function specialized for decision trees which
                 is dependent on the partition of the set of training
                 cases implied by a decision tree.",
  notes =        "Not a GP but uses a mixture of strings and trees as an
                 interpretable data structure for making a single choice
                 from two alternatives. Gives training cases a fitness
                 and choices by tournament the most difficult tests.
                 arbitrary restriction on tree to prevent learning test
                 cases rather than general principles. See also
                 \cite{siegel2}",
}

Genetic Programming entries for Eric Siegel