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Portfolios In Stochastic Local Search: Efficiently Computing Most Probable Explanations In Bayesian Networks, Ole J. Mengshoel, D Roth, D Wilkins
Portfolios In Stochastic Local Search: Efficiently Computing Most Probable Explanations In Bayesian Networks, Ole J. Mengshoel, D Roth, D Wilkins
Ole J Mengshoel
Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) …