IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on learning with Bayesian networks
Learning in graphical models
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Searching for the best elimination sequence in Bayesian networks by using ant colony optimization
Pattern Recognition Letters
Towards a More Efficient Evolutionary Induction of Bayesian Networks
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Ant Colony Optimization
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Search bias in ant colony optimization: on the role of competition-balanced systems
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multi-Objective Evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling
Computers & Mathematics with Applications
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As a powerful formalism, Bayesian networks play an increasingly important role in the Uncertainty Field. This paper proposes a hybrid method to discover the knowledge represented in Bayesian networks. The hybrid method combines dependency analysis, ant colony optimization (ACO), and the simulated annealing strategy. Firstly, the new method uses order-0 independence tests with a self-adjusting threshold value to reduce the size of the search space, so that the search process takes less time to find the near-optimal solution. Secondly, better Bayesian network models are generated by using an improved ACO algorithm, where a new heuristic function is introduced to further enhance the search effectiveness and efficiency. Finally, an optimization scheme based on simulated annealing is employed to improve the optimization efficiency in the stochastic search process of ants. In a number of experiments and comparisons, the hybrid method outperforms the original ACO-B which uses ACO and some other network learning algorithms.