A parallel framework for loopy belief propagation
Alexander Mendiburu, Roberto Santana, Jose Antonio Lozano, Endika Bengoetxea · 2007
There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them. Their main characteristic is the use of probabilistic models to represent the (in) dependencies between the variables of a concrete problem. Such probabilistic models have also been applied to the theoretical analysis of EDAs, providing a platform for the implementation of other optimization methods that can be incorporated into the EDA framework.