Title :
M-GA: A Genetic Algorithm to Search for the Best Conditional Gaussian Bayesian Network
Author :
Mascherini, Massimiliano ; Stefanini, Federico M.
Author_Institution :
Dept. of Stat., Florence Univ.
Abstract :
The search of optimal Bayesian network from a database of observations is NP-hard. Nevertheless, several heuristic search strategies have been found to be effective. We present a new population-based algorithm to learn the structure of Bayesian networks without assuming any ordering of nodes and allowing for the presence of both discrete and continuous random variables. Numerical performances of our mixed-genetic algorithm, (M-GA), are investigated on a case study taken from the literature and compared with greedy search
Keywords :
Gaussian processes; belief networks; computational complexity; genetic algorithms; greedy algorithms; inference mechanisms; random processes; search problems; NP-hard problem; greedy search; heuristic search strategy; mixed-genetic algorithm; optimal Gaussian Bayesian network search; population-based algorithm; Artificial intelligence; Bayesian methods; Databases; Genetic algorithms; Inference algorithms; Learning; Probability distribution; Production; Random variables; Statistics;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
DOI :
10.1109/CIMCA.2005.1631446
