A parallel Monte Carlo search algorithm for the conformational analysis of polypeptides

1992 ◽  
Vol 6 (2) ◽  
pp. 163-185 ◽  
Author(s):  
Daniel R. Ripoll ◽  
Stephen J. Thomas
Keyword(s):  
Monte Carlo ◽  
Conformational Analysis ◽  
Search Algorithm ◽  
Monte Carlo Search

scholarly journals MONTE-CARLO EXPRESSION DISCOVERY

2013 ◽  
Vol 22 (01) ◽  
pp. 1250035 ◽  
Author(s):  
TRISTAN CAZENAVE
Keyword(s):  
Monte Carlo ◽  
Genetic Programming ◽  
Search Algorithm ◽  
Fitness Function ◽  
Benchmark Problems ◽  
Tree Search ◽  
Confidence Bounds ◽  
Local Optima ◽  
Monte Carlo Tree Search ◽  
Monte Carlo Search

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 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 expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.

Characterization of a hybrid Monte Carlo search algorithm for structure determination

2005 ◽  
Vol 38 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Anders J. Markvardsen ◽  
Kenneth Shankland ◽  
William I. F. David ◽  
Gareth Didlick
Keyword(s):  
Monte Carlo ◽  
Crystal Structures ◽  
Structure Determination ◽  
Search Algorithm ◽  
Time Frame ◽  
Control Parameters ◽  
Acta Cryst ◽  
Hybrid Monte Carlo ◽  
Monte Carlo Search ◽  
Reasonable Time Frame

A hybrid Monte Carlo (HMC) search algorithm has recently been shown to be a promising method for structure determination from powder diffraction data [Johnston, David, Markvardsen & Shankland (2002).Acta Cryst.A58, 441–447]. Here, the performance of the algorithm on a number of different crystal structures is investigated as a function of its control parameters. This detailed analysis required the use of a system for distributed computing in order to keep the calculation times within a reasonable time frame. The results obtained confirm previous findings and a detailed discussion of the effect of the control parameters on the efficiency of the HMC method is provided. The results suggest a method for setting these parameters automatically, which is an essential step if HMC is to find routine use in the determination of crystal structures.

scholarly journals Monte Carlo Search Algorithm Discovery for Single-Player Games

2013 ◽  
Vol 5 (3) ◽  
pp. 201-213 ◽  
Author(s):  
Francis Maes ◽  
David Lupien St-Pierre ◽  
Damien Ernst
Keyword(s):  
Monte Carlo ◽  
Search Algorithm ◽  
Monte Carlo Search

Policy adaptation for vehicle routing

2020 ◽  
pp. 1-15
Author(s):  
Tristan Cazenave ◽  
Jean-Yves Lucas ◽  
Thomas Triboulet ◽  
Hyoseok Kim
Keyword(s):  
Vehicle Routing ◽  
Search Algorithm ◽  
Daily Basis ◽  
Electric Utility ◽  
Routing Problem ◽  
Utility Company ◽  
Standard Problem ◽  
Policy Adaptation ◽  
Monte Carlo Search ◽  
Player Game

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm that learns a playout policy in order to solve a single player game. In this paper we apply NRPA to the vehicle routing problem. This problem is important for large companies that have to manage a fleet of vehicles on a daily basis. Real problems are often too large to be solved exactly. The algorithm is applied to standard problem of the literature and to the specific problems of EDF (Electricité De France, the main French electric utility company). These specific problems have peculiar constraints. NRPA gives better result than the algorithm previously used by EDF.

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