Learning Bayesian Network Structures by Searching For the Best Ordering With Genetic Algorithms (original) (raw)
Related papers
Optimizing the induction of Bayesian Networks using PC and Variable Ordering Genetic Algorithms
Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BN). Previous works in the literature suggest that it is worth pursuing the use of genetic algorithms for identifying a suitable VO, when learning a BN structure from data. However, these algorithms may be computationally costly. This paper proposes a hybrid adaptive algorithm named PC-VOGA where initially the PC algorithm is performed to provide a previous VO before the genetic algorithm begins. Such previous ordering is produced by CGSort algorithm, which is also proposed in this work from BN structure induced by PC. Initial experiments revealed that the PC-VOGA approach is promising having the assistance of CGSort algorithm.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
We present a new approach to structure learning in the field of Bayesian networks: We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a "repair operator" which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer.
A Permutation Genetic Algorithm for Variable Ordering In Learning Bayesian Networks From Data
Proceedings of the Genetic and …, 2002
Greedy score-based algorithms for learning the structure of Bayesian networks may produce very different models depending on the order in which variables are scored. These models often vary significantly in quality when applied to inference. Unfortunately, finding the optimal ordering of inputs entails search through the permutation space of variables. Furthermore, in real-world applications of structure learning, the gold standard network is typically unknown. In this paper, we first present a genetic algorithm (GA) that uses a well-known greedy algorithm for structure learning (K2) and approximate inference by importance sampling as primitives in searching this permutation space. We then develop a flexible fitness measure based upon inferential loss given a specification of evidence. Finally, we evaluate this GA wrapper using the well-known networks Asia and ALARM and show that it is competitive with exhaustive enumeration in finding good orderings for K2, resulting in structures with low inferential loss under importance sampling.
Performance Analysis of an Acyclic Genetic approach to Learn Bayesian Network Structure
Indian International Conference on Artificial Intelligence, 2003
We introduce a new genetic algorithm approach for learning a Bayesian network structure from data. Our method is capable of learning over all node orderings and structures. Our encoding scheme is inherently acyclic and is capable of performing crossover on chromosomes with different node orders. We present an analysis of this approach using different Bayesian networks such as ASIA and ALARM. Results suggest that the method is effective. The tests we perform include varying the population size of the genetic algorithms, restricting the maximum number of parents a node can have, and learning with a fixed node order.
We introduce a new genetic algorithm approach for learning a Bayesian network structure from data. Our method is capable of learning over all node orderings and structures. Our encoding scheme is inherently acyclic and is capable of performing crossover on chromosomes with different node orders. We present an analysis of this approach using different Bayesian networks such as ASIA and ALARM. Results suggest that the method is effective. The tests we perform include varying the population size of the genetic algorithms, restricting the maximum number of parents a node can have, and learning with a fixed node order.
A study on the evolution of Bayesian network graph structures
2007
Bayesian Networks (BN) are often sought as useful descriptive and predictive models for the available data. Learning algorithms trying to ascertain automatically the best BN model (graph structure) for some input data are of the greatest interest for practical reasons. In this paper we examine a number of evolutionary programming algorithms for this network induction problem.