- author = "Tristan Cazenave",
- title = "Monte-Carlo Expression Discovery",
- journal = "International Journal on Artificial Intelligence Tools",
- 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 = "
http://www.lamsade.dauphine.fr/~cazenave/papers/MCExpression.pdf",
-
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 parallelise.",
-
notes = "Jargon heavy.
Universite Paris-Dauphine, 75016, Paris, France Cited by \cite{White:2015:GECCOcompa}",
