Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Real-world applications of Bayesian networks
Communications of the ACM
An introduction to genetic algorithms
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Swarm intelligence: from natural to artificial systems
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The ant colony optimization meta-heuristic
New ideas in optimization
Introduction to Reinforcement Learning
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Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
The EQ Framework for Learning Equivalence Classes of Bayesian Networks
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Toward the Formal Foundation of Ant Programming
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
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An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
On inclusion-driven learning of bayesian networks
The Journal of Machine Learning Research
Ant Colony Optimization
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
The equation for response to selection and its use for prediction
Evolutionary Computation
Journal of Artificial Intelligence Research
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A bayesian network scoring metric that is based on globally uniform parameter priors
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Causal inference in the presence of latent variables and selection bias
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On local optima in learning bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Multimedia Tools and Applications
ABC-miner: an ant-based bayesian classification algorithm
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Learning optimal bayesian networks: a shortest path perspective
Journal of Artificial Intelligence Research
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Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.