Learning Equivalence Classes of Bayesian Network with Immune Genetic Algorithm
Jian Liu · Journal of Jilin University(Science Edition) · 2009
To the question of drawbacks in learning Bayesian network with genetic algorithm,an immune genetic algorithm was proposed and used to learn the structure of Bayesian network,which combines the constraint based approach with score-search based approach.The algorithm can avoid generating illegal structures;by means of the property of Markov equivalence,the immune operators maps the search space from skeleton space to Markov equivalent class space.The experiment data show that the search space was decreased,compared with those of the genetic algorithm search in direct acyclic graph space,the convergence speed and the efficiency were improved.