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Bayesian belief networks can be used to represent and to reason about complexsystems with uncertain, incomplete and conflicting information. Belief networksare graphs encoding and quantifying probabilistic dependence and conditionalindependence among variables. One type of reasoning of interest in diagnosis iscalled abductive inference (determination of the global most probable systemdescription given the values of any partial subset of variables). In somecases, abductive inference can be performed with exact algorithms usingdistributed network computations but it is an NP-hard problem and complexityincreases drastically with the presence of undirected cycles, number ofdiscrete states per variable, and number of variables in the network. Thispaper describes an approximate method based on genetic algorithms to performabductive inference in large, multiply connected networks for which complexityis a concern when using most exact methods and for which systematic searchmethods are not feasible. The theoretical adequacy of the method is discussedand preliminary experimental results are presented.

GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks