Probabilistic Reasoning Through Genetic Algorithms and Reinforcement Learning

Xiaomin Zhong and Eugene Santos Jr., University of Connecticut

In this paper, we develop an efficient approach for inferencing 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'' crossover and ``group'' mutation for the genetic algorithm based on these groupings. 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.


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