Bayesian Network Structure Learning Using Case-Injected Genetic Algorithms

In this paper, we propose a new hybrid structure learning method that incorporates case-injected genetic algorithms as a score-and-search method for determining Bayesian network structure from data. In our approach, we first find the probabilistic dependencies among variables to constrain the search space and then employ case-injected genetic algorithms in the score-and-search phase to find a quality structure from the reduced search space. The novelty of our work lies with the introduction of combining case-based reasoning with genetic algorithms to evolve a near-optimal Bayesian network in fewer generations compared to a randomly initialized genetic algorithm. Our case-injected genetic algorithms enhance Bayesian network structure learning performance over a sequence of similar problems by extracting and storing knowledge from previously solved problems and utilizing the accumulated knowledge to solve subsequent similar problems. To evaluate the viability of our proposed approach, we conducted a series of experiments by generating a sequence of similar problems based on using data sets obtained randomly from three well-known benchmark Bayesian networks. We also compared the performance of our proposed approach with the state-of-the-art algorithm. Our preliminary results show that case-injected genetic algorithms provide better performance in learning Bayesian network structure compared to GA and the state-of-the-art algorithm. Our proposed approach has applications in real-world domains such as e-commerce system and health care.