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AUSAI
2004
Springer

An ACO Algorithm for the Most Probable Explanation Problem

9 years 21 days ago
An ACO Algorithm for the Most Probable Explanation Problem
We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference. After tuning its parameters settings, we compare ANTMPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search. Experimental results on both artificial and real networks show that in general ANT-MPE outperforms all other algorithms, but on networks with unskewed distributions local search algorithms are slightly better. The result reveals the nature of ACO as a combination of both sampling and local search. It helps us to understand ACO better, and, more important, it also suggests a possible way to improve ACO.
Haipeng Guo, Prashanth R. Boddhireddy, William H.
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where AUSAI
Authors Haipeng Guo, Prashanth R. Boddhireddy, William H. Hsu
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