This paper present a general method coupling genetic algorithms and Monte-Carlo simulation to address simulation optimization issues in the field of engineering asset management. After a description of the method, parameters tuning issues are analyzed through a test-case.

 AI algorithms like genetic algorithms and automated opponents.  Random game content and level generation.  Simulation of complex phenomena such as weather and fire.  Numerical methods such as Monte-Carlo integration.  Until recently primality proving used randomized algorithms.  Cryptography algorithms such as RSA use random numbers for key generation.  Weather simulation and other stat...

one of known methods for measuring, forecasting and managing risk is value at risk, which recently has been used by financial institutions extensively. value at risk (var) is a method for recognizing and evaluating risk and uses standard statistical techniques that have daily using in other contexts. this research is seeking a career for managing investment risk in stock exchange and selection ...

background: inclusion of inhomogeneity corrections in intensity modulated small fields always makes conformal irradiation of lung tumor very complicated in accurate dose delivery. objective: in the present study, the performance of five algorithms via monte carlo, pencil beam, convolution, fast superposition and superposition were evaluated in lung cancer intensity modulated radiotherapy planni...

background: the monte carlo method is the most accurate method for simulation of radiation therapy equipment. the linear accelerators are currently the most widely used machines in radiation therapy centers. methods: in this work, a monte carlo modelling of the siemens oncor linear accelerator in 6 mv and 18 mv beams was performed. the results of simulation were validated by measurements in wat...

Algorithms developed to enable the use of atornistic molecular simulation methods with parallel computers are reviewed. Methods appropriate for bonded as well as non-bonded (and charged) interactions are included. While strategies for obtaining parallel molecukir simulations have been developed for the full variety of atomistic simulation methods, molecular dynamics and Monte Carlo have receive...

Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov c...

We present a new approach to perform molecular simulations using evolutionary algorithms. The main application of this work 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 s...

introduction: the key characteristics of positron emission tomography (pet) are its quantitative capability and its sensitivity, which allow the in vivo imaging of biochemical interactions with small amounts of tracer concentrations. therefore, accurate quantification is important. however, it can be sensitive to several physical factors. the aim of this investigation is the assessment of the e...