A New Structure Learning of Bayesian Networks and Its Application to Context Aware Computing in Home Network System: Genetic Approach

In this paper, we suggest the model for the context aware computing of the home network systems. Our model uses the Bayesian networks (BNs) and a new approach to structure learning of BNs based on genetic algorithm is proposed. The proposed method explores the wider solution space than the previous method which also uses the genetic algorithm for structure learning of BNs. In the previous method, while the ordering among the nodes of the BNs was fixed their conditional dependencies represented by the connectivity matrix was learned, whereas, in the proposed method, the ordering as well as the conditional dependency among the BN nodes is learned. To implement this method using the genetic algorithm, we represent an individual of the population as a pair of chromosomes: The first one represents the ordering among the BN nodes and the second one represents their conditional dependencies based on the ordering of the first chromosome. To implement proposed method new crossover and mutation operations which are closed in the set of the admissible individuals are introduced. Finally, the proposed method is applied to the given context aware problem of home network system and its effectiveness is demonstrated. Key-Words: Bayesian network, Genetic algorithms, Context awareness, Structure learning, A pair of chromosomes