A typical characteristic of Bayesian network topology is dependences of each variable within the network, which makes it impossible to optimize variables. This problem is solved by the developed approach to Bayesian network construction based on Self-organizing Genetic Algorithm (SGA) from knowledge base. The Genetic Algorithm (GA) is improved by self-organizing organism and an effective operator is provided to search the global optimum value in order to avoid an early convergence for a normal GA algorithm. At last the experiment results and the convergence of SGA are discussed.
Index Terms:
Self-organizing Genetic Algorithm (SGA), Bayesian network, Bayesian learning based on Knowledge base, network safety applies
Citation:
T.J. Jia, "Construction of Learning Algorithm based on SGA Bayesian Network," isecs, pp.37-40, 2008 International Symposium on Electronic Commerce and Security, 2008