Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Cost-based abduction and MAP explanation
Artificial Intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
An algorithm for finding MAPs for belief networks through cost-based abduction
Artificial Intelligence
The complexity of approximating MAPs for belief networks with bounded probabilities
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Parallel Genetic Heuristic for the Quadratic Assignment Problem
Proceedings of the 3rd International Conference on Genetic Algorithms
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Bayesian belief networks (BBN's) are a popular graphical representation for reasoning under (probabilistic) uncertainty. An important, and NP-complete, problem on BBN's is the maximum a posteriori (MAP) assignment problem, in which the goal is find the network assignment with highest conditional probability given a set of observances, or evidence. In this paper, we present an adaptive hybrid technique combining genetic algorithms and simulated annealing, and apply it to a layered 70-node BBN. Simulated annealing is used as a type of mutation within the framework of the genetic algorithm, and the parameters of the annealing schedule are themselves adapted by the genetic algorithm.