Title :
Construction of Learning Algorithm based on SGA Bayesian Network
Author_Institution :
Coll. of Electron. & Inf., Shanghai Dianji Univ., Shanghai
Abstract :
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.
Keywords :
belief networks; genetic algorithms; learning (artificial intelligence); Bayesian network construction; Bayesian network topology; learning algorithm; self-organizing genetic algorithm; self-organizing organism; Bayesian methods; Convergence; Educational institutions; Electronic commerce; Genetic algorithms; Information security; Network topology; Organisms; Polynomials; Uncertainty; Bayesian learning based on Knowledge base; Bayesian network; Self-organizing Genetic Algorithm (SGA); network safety applies;
Conference_Titel :
Electronic Commerce and Security, 2008 International Symposium on
Conference_Location :
Guangzhou City
Print_ISBN :
978-0-7695-3258-5
DOI :
10.1109/ISECS.2008.23
