Simulated Annealing Using Hybrid Monte Carlo
نویسنده
چکیده
We propose a variant of the simulated annealing method for optimization in the multivhriate analysis of differentiable functions. The method uses global actualizations via the hybrid Monte Carlo algorithm in their generalized version for the proposal of new configurations. We show how this choice can improve upon the performance of simulated annealing methods (mainly when the number of variables is large) by allowing a more effective searching scheme and a faster annealing schedule.
منابع مشابه
Hybrid Simulated Annealing
We propose a variant of the Simulated Annealing method for optimization in the multivariate analysis of diierentiable functions. The method uses the Hybrid Monte Carlo algorithm for the proposal of new conngurations. We show how this choice can improve the performance of simulated annealing methods by allowing much faster annealing schedules.
متن کاملA Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model
The hydrophobic-polar model has been widely studied in the field of protein structure prediction both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization and Markov Chain Monte Carlo that we called Hybrid Monte Carlo Ant Colony Optimization. We describe this method and compare results obtained o...
متن کاملBayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method
It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the \Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of training cases to be approximated arbitrarily closely, in contrast to previous approaches which approximate the posterior weight distribution by a Gaussian. In this work, the Hybri...
متن کاملMarkov Chain Monte Carlo Methods for Radiation Hybrid Mapping
The ordering of genetic loci is central to genetic mapping at all levels. Markov chain Monte Carlo (MCMC) techniques can provide estimates of the posterior density of orders while accounting naturally for missing data, data errors, and unknown parameters. MCMC sampling schemes have been proposed for mapping problems such as linkage mapping and radiation hybrid mapping. The sampling schemes tend...
متن کاملComparing Monte Carlo methods for finding ground states of Ising spin glasses: Population annealing, simulated annealing, and parallel tempering.
Population annealing is a Monte Carlo algorithm that marries features from simulated-annealing and parallel-tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population-annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of popu...
متن کامل