Monte Carlo Simulation and Global Optimization without Parameters
نویسندگان
چکیده
منابع مشابه
Monte Carlo simulation and global optimization without parameters.
We propose a new ensemble for Monte Carlo simulations, in which each state is assigned a statistical weight 1/k, where k is the number of states with smaller or equal energy. This ensemble has robust ergodicity properties and gives significant weight to the ground state, making it effective for hard optimization problems. It can be used to find free energies at all temperatures and picks up asp...
متن کاملMonte Carlo Simulation and Population-Based Optimization
This paper briefly reviews some properties of Monte Carlo simulation and emphasizes the link to evolutionary computation. It shows how this connection can help to study evolutionary algorithms within a unified framework. It also gives some practical examples of implementation inspired from MOSES (the mutation-or-selection evolution strategy).
متن کاملMonte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis
Monte Carlo simulation (MCS) has been widely used for the uncertainty propagations of building simulation tools. In general, most unknown inputs for the MCS are regarded as single probability distributions based on experts’ subjective judgements and assumptions, when simulation information and measured data are inaccurate and insufficient. However, this can lead to meaningless and untrustworthy...
متن کاملMonte Carlo Simulation of a Linear Accelerator and Electron Beam Parameters Used in Radiotherapy
Introduction: In recent decades, several Monte Carlo codes have been introduced for research and medical applications. These methods provide both accurate and detailed calculation of particle transport from linear accelerators. The main drawback of Monte Carlo techniques is the extremely long computing time that is required in order to obtain a dose distribution with good statistical accuracy. ...
متن کاملBiopolymer structure simulation and optimization via fragment regrowth Monte Carlo.
An efficient exploration of the configuration space of a biopolymer is essential for its structure modeling and prediction. In this study, the authors propose a new Monte Carlo method, fragment regrowth via energy-guided sequential sampling (FRESS), which incorporates the idea of multigrid Monte Carlo into the framework of configurational-bias Monte Carlo and is suitable for chain polymer simul...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review Letters
سال: 1995
ISSN: 0031-9007,1079-7114
DOI: 10.1103/physrevlett.74.2151