Differential-evolution-based coevolution ant colony optimization algorithm for Bayesian network structure learning
type: Item
instance of: scholarly article
title: Differential-evolution-based coevolution ant colony optimization algorithm for Bayesian network structure learning
zbMATH Open document ID: 1461.90202
author: Xiangyin Zhang
author: Yuying Xue
author: Xingyang Lu
author: Songmin Jia
published in: Algorithms
publication date: 2019-01-21T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90B15
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90C35
zbMATH DE Number: 7003839
zbMATH Keywords: Bayesian network
zbMATH Keywords: ant colony optimization
zbMATH Keywords: structure learning
zbMATH Keywords: cooperative evolution
zbMATH Keywords: differential evolution
describes a project that uses: BNT
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a11110188
OpenAlex ID: W2901364239
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DOI: 10.3390/A11110188
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description: scientific article; zbMATH DE number 7003839