Directing genetic algorithms for probabilistic reasoning through reinforcement learning

In this paper, we develop an efficient online approach for belief revision over Bayesian networks by using a reinforcement learning controller to direct a genetic algorithm. The random variables of a Bayesian network can be grouped into several sets reflecting the strong probabilistic correlations between random variables in the group. We build a reinforcement learning controller to identify these groups and recommend the use of "group" mutation and "group" crossover for the genetic algorithm based on these groupings online. The system then evaluates the performance of the genetic algorithm based on these groupings online. The system then evaluates the performance of the genetic algorithm and continues with reinforcement learning to further tune the controller to search for a better grouping.