Abstract (EN)

Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Pro-gramming evaluates and combines trees to discover expressions that maximize 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 ex-pression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize. [ABSTRACT FROM AUTHOR]