Structure Learning of Bayesian Networks Using Dual Genetic Algorithm

Jaehun LEE; Wooyong CHUNG; Euntai KIM

doi:10.1093/ietisy/e91-d.1.32

Structure Learning of Bayesian Networks Using Dual Genetic Algorithm

Jaehun LEE, Wooyong CHUNG, Euntai KIM

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A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.1 pp.32-43

Publication Date: 2008/01/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.1.32

Type of Manuscript: PAPER

Category: Artificial Intelligence and Cognitive Science

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Jaehun LEE, Wooyong CHUNG, Euntai KIM, "Structure Learning of Bayesian Networks Using Dual Genetic Algorithm" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 1, pp. 32-43, January 2008, doi: 10.1093/ietisy/e91-d.1.32.
Abstract: A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.1.32/_p

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@ARTICLE{e91-d_1_32,
author={Jaehun LEE, Wooyong CHUNG, Euntai KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Structure Learning of Bayesian Networks Using Dual Genetic Algorithm},
year={2008},
volume={E91-D},
number={1},
pages={32-43},
abstract={A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.},
keywords={},
doi={10.1093/ietisy/e91-d.1.32},
ISSN={1745-1361},
month={January},}

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TY - JOUR
TI - Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 32
EP - 43
AU - Jaehun LEE
AU - Wooyong CHUNG
AU - Euntai KIM
PY - 2008
DO - 10.1093/ietisy/e91-d.1.32
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2008
AB - A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.
ER -