Annealing stochastic approximation Monte Carlo algorithm for neural network training

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Annealing Stochastic Approximation Monte Carlo for Global Optimization

During the past several decades, simulated annealing (Kirkpatrick et al., 1983) and the genetic algorithm (Holland, 1975; Goldberg, 1989) have been applied successfully by many authors to highly complex optimization problems in different fields of sciences and engineering. In spite of their successes, both algorithms suffer from some difficulties in convergence to the global optima. Suppose tha...

Stochastic Approximation Monte Carlo for MLP Learning

Over the past several decades, multilayer perceptrons (MLPs) have achieved increased popularity among scientists, engineers, and other professionals as tools for knowledge representation. Unfortunately, there is no a universal architecture which is suitable for all problems. Even with the correct architecture, frustrating problems of connection weights training still remain due to the rugged na...

A Stochastic Monte Carlo Algorithm

We propose a Monte Carlo algorithm to promote Kennedy and Kuti’s linear accept/reject algorithm which accommodates unbiased stochastic estimates of the probability to an exact one. The probability upper bound violations are avoided by adopting the Metropolis accept/reject steps for both the dynamical and noise configurations and the lower bound violations can be absorbed into the observables. W...

Stochastic Approximation in Monte Carlo Computation

The Wang–Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for a physical system. A remarkable feature of the WL algorithm is that it is not trapped by local energy minima, which is very important for systems with rugged energy landscapes. This feature has led to many successful applications of the algorithm in statistical physics and...