The traveling salesman problem with time windows is known to be a really difficult benchmark for optimization algorithms. In this paper, we are interested in the minimization of the travel cost. To solve this problem, we propose to use the nested Monte-Carlo algorithm combined with a Self-Adaptation Evolution Strategy. We compare the efficiency of several fitness functions. We show that with our technique we can reach the state of the art solutions for a lot of problems in a short period of time.

Abstract. We present a new exploration term, more efficient than classical UCT-like exploration terms and combining efficiently expert rules, patterns extracted from datasets, All-Moves-As-First values and classical online values. As this improved bandit formula does not solve several important situations (semeais, nakade) in computer Go, we present three other important improvements which are central in the recent progress of our program MoGo:-We show an expert-based improvement of Monte-Carlo simulations for nakade situations; we also emphasize some limitations of this modification. -We show a technique which preserves diversity in the Monte-Carlo simulation, which greatly improves the results in 19x19. -Whereas the UCB-based exploration term is not efficient in MoGo, we show a new exploration term which is highly efficient in MoGo. MoGo recently won a game with handicap 7 against a 9Dan Pro player, Zhou JunXun, winner of the LG Cup 2007, and a game with handicap 6 against a 1Dan pro player, Li-Chen Chien. 1