When looking for relevant mutations of a learning program, a main trouble is that evaluating a mutation is noisy; we can have a precise estimate of a mutation, if we test it many times, but this is quite expensive; or we can have a rough estimate, which is much faster. This is a load balancing problem: on which mutations should we spend more effort ? Bandit algorithms have been used for this load balancing: they choose the com-putational effort spent on various possible mutations, depending on the current estimate of the quality of a mutation and on the precision of this estimate. How-ever, in many cases, we want to validate some possible mutations; when should we stop the bandit mutation, and analyze new mutations ? Racing algorithms are aimed at combining the load balancing and the statistical validation; we here mathematically analyze and experiment racing algorithms in the context of mu-tations of programs, i.e. genetic programming. As an application, we consider Monte-Carlo Tree Search. Monte-Carlo Tree Search is a recent very successful algorithm for reinforcement learning, success-fully applied in games and Markov decision Processes. We consider the valida-tion of randomly generated patterns in a Monte-Carlo Tree Search program. Our bandit-based genetic programming (BGP) algorithm, with proved mathematical properties, outperformed a highly optimized handcrafted module of a well-known computer-Go program with several world records in the game of Go.