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2006

MEDAL Report 2006005
Order or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability?
Martin Pelikan and James D. Laury, Jr.  (2006)

Abstract. It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This paper demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible.


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Missouri Estimation of Distribution Algorithms Laboratory
Department of Mathematics and Computer Science
University of Missouri-St. Louis, St. Louis, MO

                   

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