Title :
Improving molecular simulation: a meta optimisation of Monte Carlo parameters
Author :
Leblanc, Benoit ; Lutton, Evelyne ; Braunschweig, Bertrand ; Toulhoat, Hervé
Abstract :
We present a new approach to performing molecular simulations using evolutionary algorithms. The main application is the simulation of dense amorphous polymers and the goal is to improve the efficiency of sampling, in other words to obtain valid samples from the phase state more rapidly. Our approach is based on parallel Markovian Monte Carlo simulations of the same physico-chemical system, where we optimise some Monte Carlo parameters by means of a real coded genetic algorithm
Keywords :
Markov processes; Monte Carlo methods; digital simulation; genetic algorithms; physics computing; Monte Carlo parameters; dense amorphous polymers; evolutionary algorithms; meta optimisation; molecular simulations; parallel Markovian Monte Carlo simulations; physico-chemical system; real coded genetic algorithm; Amorphous materials; Computational modeling; Evolutionary computation; Fractals; Genetics; Monte Carlo methods; Polymers; Potential energy; Proteins; Sampling methods;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934433
