Compound Derivations in Fuzzy Genetic Programming

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@InProceedings{GeyerSchulz96e,
  author =       "Andreas Geyer--Schulz",
  title =        "Compound Derivations in Fuzzy Genetic Programming",
  booktitle =    "1996 Biennial Conference of the North American Fuzzy
                 Information Processing Society, NAFIPS",
  year =         "1996",
  month =        jul,
  pages =        "510--514",
  DOI =          "doi:10.1109/NAFIPS.1996.534787",
  keywords =     "genetic algorithms, genetic programming, a priori
                 knowledge, compound derivations, context-free language,
                 equivalence transformations, fuzzy genetic programming,
                 genetic algorithms, grammar, k-bounded context-free
                 languages, lambda abstraction, machine-learning method,
                 nonlinear transformations, speedup theorems,
                 context-free languages, fuzzy logic, genetic
                 algorithms, grammars, heuristic programming, learning
                 (artificial intelligence)",
  size =         "5 pages",
  abstract =     "We introduce the concept of compound derivations in
                 fuzzy genetic programming as an alternative to lambda
                 abstraction. We show that in fuzzy genetic programming
                 based on simple genetic algorithms over k-bounded
                 context-free languages compound derivations provide a
                 powerful tool for generating automatically equivalence
                 transformations on the grammar of a context-free
                 language. Although such transformations do not change
                 the language generated by the grammar, the probability
                 of generating words can be transformed almost at will.
                 We apply this property to: nonlinear transformations of
                 the probability of generating words for initialising a
                 population,; incorporating a priori knowledge; the new
                 genetic operator compound which provides an alternative
                 to lambda abstraction; and proving speedup theorems",
}

Genetic Programming entries for Andreas Geyer-Schulz

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