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.
M-GA: A Genetic Algorithm to Search for the Best Conditional Gaussian Bayesian Network / M.MASCHERINI ; F. M. STEFANINI. - STAMPA. - 2:(2006), pp. 61-67. (Intervento presentato al convegno International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05) tenutosi a Vienna nel 2005).
M-GA: A Genetic Algorithm to Search for the Best Conditional Gaussian Bayesian Network
STEFANINI, FEDERICO MATTIA
2006
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.
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