Causality of Hierarchical Variable Length Representations

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  author =       "Christian Igel",
  title =        "Causality of Hierarchical Variable Length
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "324--329",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, coding,
                 hierarchical variable-length representations, problem
                 difficulty, program tree representations, quantitative
                 probabilistic causality measure, search space metric,
                 statistical fitness landscape analysis, strong
                 causality, tree edit distance, probability, program
                 control structures, programming theory, tree
  ISBN =         "0-7803-4869-9",
  URL =          "",
  DOI =          "doi:10.1109/ICEC.1998.699753",
  size =         "6 pages",
  abstract =     "In this paper, the strong causality of program tree
                 representations is considered. A quantitative,
                 probabilistic causality measure is used in contrast to
                 statistical fitness landscape analysis methods.
                 Although it fails to rank different problems according
                 to their difficulty, it is helpful for choosing the
                 right coding for a given task. The investigation uses a
                 metric on the search space called the tree edit
                 distance. Different ways to define such a measure are
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",

Genetic Programming entries for Christian Igel