Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures
Thierry, Alain Delaplace, Muhammad Muzzamil, Hubert Cardot, Jean-Yves Ramel · Sciyo eBooks · 2010
We have presented three methods for learning the structure of a Bayesian network. The first one consists in the control of the probability distribution of mutation in the genetic algorithm. The second one is to incorporate a scheme penalty in the genetic algorithm so that it avoids certain areas of space research. The third method is to search through several competing populations and to allow timely exchange among these populations. We have shown experimentally that different algorithms behaved satisfactorily, in particular that they were proving to be successful on large databases. We also examined the behavior of proposed algorithms. Niching strategies are interesting, especially using the spatial one, which focuses quickly on the best solutions.