Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Valuation-based systems for Bayesian decision analysis
Operations Research
Studies on hypergraphs I: hyperforests
Discrete Applied Mathematics
Niching methods for genetic algorithms
Niching methods for genetic algorithms
A Comparison of Graphical Techniques for Asymmetric Decision Problems
Management Science
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Strategic Decision Making
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms
Statistics and Computing
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Efficient Approximation for Triangulation of Minimum Treewidth
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Directed reduction algorithms and decomposable graphs
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Variable period adaptive genetic algorithm
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A practical algorithm for finding optimal triangulations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Welldefined decision scenarios
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Probabilistic inference in influence diagrams
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Expert Systems with Applications: An International Journal
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
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Influence diagrams are powerful tools for representing and solving complex inference and decision-making problems under uncertainty. They are directed acyclic graphs with nodes and arcs that have a precise meaning. The algorithm for evaluating an influence diagram deletes nodes from the graph in a particular order given by the position of each node and its arcs with respect to the value node. In many cases, however, there is more than one possible node deletion sequence. They all lead to the optimal solution of the problem, but may involve different computational efforts, which is a primary issue when facing real-size models. Finding the optimal deletion sequence is a NP-hard problem. The proposals given in the literature have proven to require complex transformations of the influence diagram. In this paper, we present a genetic algorithm-based approach, which merely has to be added to the influence diagram evaluation algorithm we use, and whose codification is straightforward. The experiments, varying parameters like crossover and mutation operators, population sizes and mutation rates, are analysed statistically, showing favourable results over existing heuristics.