14 citations · 22 across the 8 of their papers we have counts for
8 papers
Mean field variational framework for integer optimization
Arturo Berrones, Jonás Velasco, Juan Banda
A principled method to obtain approximate solutions of general constrained integer optimization problems is introduced. The approach is based on the calculation of a mean field pro…
An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization
Jonás Velasco, Mario A. Saucedo-Espinosa, Hugo Jair Escalante +5
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of…
Bayesian Inference Based on Stationary Fokker-Planck Sampling
Arturo Berrones
A novel formalism for Bayesian learning in the context of complex inference models is proposed. The method is based on the use of the Stationary Fokker--Planck (SFP) approach to sa…
Characterization of the convergence of stationary Fokker-Planck learning
Arturo Berrones
The convergence properties of the stationary Fokker-Planck algorithm for the estimation of the asymptotic density of stochastic search processes is studied. Theoretical and empiric…
Stationary probability density of stochastic search processes in global optimization
Arturo Berrones
A method for the construction of approximate analytical expressions for the stationary marginal densities of general stochastic search processes is proposed. By the marginal densit…
Knowledge Network Approach to Noise Reduction
Arturo Berrones
Previous preliminary results on the application of knowledge networks to noise reduction in stationary harmonic and weakly chaotic signals are extended to more general cases. The f…