Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Tradit...

Nonlinear multimodal filtering problems are usually addressed via Monte Carlo algorithms. These algorithms involve sampling procedures that are similar to proportional selection in genetic algorithms, and that are prone to failure due to genetic drift. This work investigates the feasibility and the relevance of niching strategies in this context. Sharing methods are evaluated experimentally, an...

The simulation of diffusion-based molecular communication systems with absorbing receivers often requires a high computational complexity to produce accurate results. In this work, a new a priori Monte Carlo (APMC) algorithm is proposed to precisely simulate the molecules absorbed at a spherical receiver when the simulation time step length is relatively large. This algorithm addresses the limi...

Consider an undeveloped oilfield with uncertainty about the size and quality of its reserves. There are some alternatives to invest in information to reduce the risk and to reveal some characteristics of the reserve. This paper presents an evolutionary real options model of optimization under uncertainty with genetic algorithms and Monte Carlo simulation, to select the best alternative of inves...

This paper proposes an approach towards modeling an actor system, especially suited to describe a company’s organization, based on game theory [11] and learning-based (evolutionary) local optimization. This method relies on the combination of three techniques: sampling for simulation (Monte-Carlo), game theory as far as the search for equilibrium is concerned and heuristic local search methods,...

Monte Carlo methods, in particular those based on Markov chains and on interacting particle systems, are by now tools that are routinely used in machine learning. These methods have had a profound impact on statistical inference in a wide range of application areas where probabilistic models are used. Moreover, there are many algorithms in machine learning which are based on the idea of process...

We review a family of local algorithms that permit the simulation of charged particles with purely local dynamics. Molecular dynamics formulations lead to discretizations similar to those of “particle in cell” methods in plasma physics. We show how to formulate a local Monte-Carlo algorithm in the presence of the long ranged Coulomb interaction. We compare the efficiencies of our molecular dyna...