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Differential-evolution-based coevolution ant colony optimization algorithm for Bayesian network structure learning. (English) Zbl 07003839
Summary: Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy.
MSC:
90C59 Approximation methods and heuristics in mathematical programming
90B15 Stochastic network models in operations research
68T05 Learning and adaptive systems in artificial intelligence
90C35 Programming involving graphs or networks
Software:
BNT
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