
An efficient approach for finding the MPE in belief networks
Given a belief network with evidence, the task of finding the I most pro...
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Distributed Revision of Belief Commitment in MultiHypothesis Interpretations
This paper extends the applications of beliefnetworks to include the re...
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GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks
Bayesian belief networks can be used to represent and to reason about co...
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Optimization of Reliability of Network of Given Connectivity using Genetic Algorithm
Reliability is one of the important measures of how well the system meet...
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A Cooperative Coevolutionary Genetic Algorithm for Learning Bayesian Network Structures
We propose a cooperative coevolutionary genetic algorithm for learning B...
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Cryptanalysis of MerkleHellman cipher using parallel genetic algorithm
In 1976, Whitfield Diffie and Martin Hellman introduced the public key c...
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RealWorld Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method
Airline crew cost is the secondlargest operating cost component and its...
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Reasoning and Facts Explanation in Valuation Based Systems
In the literature, the optimization problem to identify a set of composite hypotheses H, which will yield the k largest P(HS_e) where a composite hypothesis is an instantiation of all the nodes in the network except the evidence nodes KSy:93 is of significant interest. This problem is called "finding the k Most Plausible Explanation (MPE) of a given evidence S_e in a Bayesian belief network". The problem of finding k most probable hypotheses is generally NPhard Cooper:90. Therefore in the past various simplifications of the task by restricting k (to 1 or 2), restricting the structure (e.g. to singly connected networks), or shifting the complexity to spatial domain have been investigated. A genetic algorithm is proposed in this paper to overcome some of these restrictions while stepping out from probabilistic domain onto the general Valuation based System (VBS) framework is also proposed by generalizing the genetic algorithm approach to the realm of DempsterShafer belief calculus.
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