A parallel framework for loopy belief propagation

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. Some of these methods, typically used for probabilistic inference, are belief propagation algorithms. In this paper we present a parallel approach for one of these inference-based algorithms, the loopy belief propagation algorithm for factor graphs. Our parallel implementation was designed to provide an algorithm that can be executed in clusters of computers or multiprocessors in order to reduce the total execution time. In addition, this framework was also designed...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Alexander Mendiburu, Roberto Santana, Jose Antonio Lozano, Endika Bengoetxea
• Keywords: Belief Propagation Algorithm, GECCO 2007, Probabilistic Models, Such Probabilistic Models