The traveling salesman problem with time windows is known to be a really difficult benchmark for ... more 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.
In this paper, we are interested in the minimization of the travel cost of the traveling salesman... more In this paper, we are interested in the minimization of the travel cost of the traveling salesman problem with time windows. In order to do this minimization we use a Nested Rollout Policy Adaptation (NRPA) algorithm. NRPA has multiple levels and maintains the best tour at each level. It consists in learning a rollout policy at each level. We also show how to improve the original algorithm with a modified rollout policy that helps NRPA to avoid time windows violations.
Monte Carlo Tree Search is a recent algorithm that achieves more and more successes in various do... more Monte Carlo Tree Search is a recent algorithm that achieves more and more successes in various domains. We propose an improvement of the Monte Carlo part of the algorithm by modifying the simulations depending on the context. The modification is based on a reward function learned on a tiling of the space of Monte Carlo simulations. The tiling is done by regrouping the Monte Carlo simulations where two moves have been selected by one player. We show that it is very efficient by experimenting on the game of Havannah.
2013 IEEE Symposium on Computational Intelligence in Scheduling (CISched), 2013
ABSTRACT The well-known traveling salesman problem (TSP) is concerned with determining the shorte... more ABSTRACT The well-known traveling salesman problem (TSP) is concerned with determining the shortest route for a vehicle while visiting a set of cities exactly once. We consider knowledge and algorithm engineering in combinatorial optimization for improved solving of complex TSPs with Time Windows (TSPTW). To speed-up the exploration of the applied Nested Monte-Carlo Search with Policy Adaption, we perform beam search for an improved compromise of search breadth and depth as well as automated knowledge elicitation to seed the distribution for the exploration. To evaluate our approach, we use established TSPTW benchmarks with promising results. Furthermore, we indicate improvements for real-world logistics by its use in a multiagent system. Thereby, each agent computes individual TSPTW solutions and starts negotiation processes on this basis.
Monte-Carlo Tree Search is now a well established algorithm, in games and beyond. We analyze its ... more Monte-Carlo Tree Search is now a well established algorithm, in games and beyond. We analyze its scalability, and in particular its limitations, and the implications in terms of parallelization, in particular for our program MoGo but also for our Havannah program Shakti. In particular, we get a good efficiency for the parallel versions, both for multicore machines and for message-passing machines, but in spite of promising results in self-play there are situations for which increasing the time per move does not solve anything, and therefore parallelization is not the solution either. Nonetheless, for problems on which the Monte-Carlo part is less biased than in Go, parallelization should be very efficient even without shared memory.
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11, 2011
We compare various approaches for multimodal optimization; we focus on comparing restart and more... more We compare various approaches for multimodal optimization; we focus on comparing restart and more sophisticated approaches, and on the use of quasi-random numbers.
The Monte-Carlo Tree Search algorithm has been successfully applied in various domains. However, ... more The Monte-Carlo Tree Search algorithm has been successfully applied in various domains. However, its performance heavily depends on the Monte-Carlo part. In this paper, we propose a generic way of improving the Monte-Carlo simulations by using RAVE values, which already strongly improved the tree part of the algorithm. We prove the generality and efficiency of our approach by showing improvements on two different applications: the game of Havannah and the game of Go.
In this paper, we study the optimization of a neural network used for controlling a Monte-Carlo T... more In this paper, we study the optimization of a neural network used for controlling a Monte-Carlo Tree Search (MCTS/UCT) algorithm. The main results are: (i) the specification of a new multimodal benchmark function; this function has been defined in particular in agreement with [1] which has pointed out that most multimodal functions are not satisfactory for some real-world multimodal scenarios (section 2); (ii) experimentation of Evolution Strategies on this new multimodal benchmark function, showing the great efficiency of quasi-random mutations in this framework (section 3); (iii) the proof-of-concept of the application of ES for grid-based tuning Neural Networks for controlling MCTS/UCT (see section 3).
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Papers by Fabien Teytaud