A Cooperative Coevolutionary Genetic Algorithm for Learning Bayesian Network Structures
This work addresses the challenge of efficient Bayesian network structure learning for data analysis, but it appears incremental as it builds on existing genetic algorithm approaches with a coevolutionary twist.
The authors tackled the problem of learning Bayesian network structures from fully observable data by proposing a cooperative coevolutionary genetic algorithm that decomposes the task into node ordering and connectivity matrix subproblems, and they showed it outperforms the deterministic K2 algorithm in simulations on Alarm and Insurance networks.
We propose a cooperative coevolutionary genetic algorithm for learning Bayesian network structures from fully observable data sets. Since this problem can be decomposed into two dependent subproblems, that is to find an ordering of the nodes and an optimal connectivity matrix, our algorithm uses two subpopulations, each one representing a subtask. We describe the empirical results obtained with simulations of the Alarm and Insurance networks. We show that our algorithm outperforms the deterministic algorithm K2.