Monte-Carlo Expression Discovery

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  author =       "Tristan Cazenave",
  title =        "Monte-Carlo Expression Discovery",
  journal =      "International Journal on Artificial Intelligence
  year =         "2013",
  volume =       "22",
  number =       "1",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, MCTS,
                 Monte-Carlo tree search, expression discovery, nested
                 Monte-Carlo search, upper confidence bounds for trees,
                 UCT, bloat",
  ISSN =         "0218-2130",
  URL =          "",
  DOI =          "doi:10.1142/S0218213012500352",
  size =         "21 pages",
  abstract =     "Monte-Carlo Tree Search is a general search algorithm
                 that gives good results in games. Genetic Programming
                 evaluates and combines trees to discover expressions
                 that maximise a given fitness function. In this paper
                 Monte-Carlo Tree Search is used to generate expressions
                 that are evaluated in the same way as in Genetic
                 Programming. Monte-Carlo Tree Search is transformed in
                 order to search expression trees rather than lists of
                 moves. We compare Nested Monte-Carlo Search to UCT
                 (Upper Confidence Bounds for Trees) for various
                 problems. Monte-Carlo Tree Search achieves state of the
                 art results on multiple benchmark problems. The
                 proposed approach is simple to program, does not suffer
                 from expression growth, has a natural restart strategy
                 to avoid local optima and is extremely easy to
  notes =        "Jargon heavy.

                 Universite Paris-Dauphine, 75016, Paris, France Cited
                 by \cite{White:2015:GECCOcompa}",

Genetic Programming entries for Tristan Cazenave