Article

A chain-model genetic algorithm for Bayesian network structure learning

Authors:

Frank Herrmann,

John McCallAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 1264 - 1271

https://doi.org/10.1145/1276958.1277200

Published: 07 July 2007 Publication History

Abstract

Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]. The super exponential growth of the number of possible networks given the number of factors in the studied problem domain has meant that more often, approximate and heuristic rather than exact methods are used. In this paper, a novel genetic algorithm approach for reducing the complexity of Bayesian network structure discovery is presented. We propose a method that uses chain structures as a model for Bayesian networks that can be constructed from given node orderings. The chain model is used to evolve a small number of orderings which are then injected into a greedy search phase which searches for an optimal structure. We present a series of experiments that show a significant reduction can be made in computational cost although with some penalty in success rate.

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Lahiri ARastogi KDatta ASeptiningsih E(2021)Bayesian Network Analysis of Lysine Biosynthesis Pathway in RiceInventions10.3390/inventions60200376:2(37)Online publication date: 24-May-2021
https://doi.org/10.3390/inventions6020037
Adee AGansch RLiggesmeyer PGlaeser CDrews F(2021)Discovery of Perception Performance Limiting Triggering Conditions in Automated Driving2021 5th International Conference on System Reliability and Safety (ICSRS)10.1109/ICSRS53853.2021.9660641(248-257)Online publication date: 24-Nov-2021
https://doi.org/10.1109/ICSRS53853.2021.9660641
Adee AGansch RLiggesmeyer P(2021)Systematic Modeling Approach for Environmental Perception Limitations in Automated Driving2021 17th European Dependable Computing Conference (EDCC)10.1109/EDCC53658.2021.00022(103-110)Online publication date: Sep-2021
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Index Terms

A chain-model genetic algorithm for Bayesian network structure learning
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Lahiri ARastogi KDatta ASeptiningsih E(2021)Bayesian Network Analysis of Lysine Biosynthesis Pathway in RiceInventions10.3390/inventions60200376:2(37)Online publication date: 24-May-2021
https://doi.org/10.3390/inventions6020037
Adee AGansch RLiggesmeyer PGlaeser CDrews F(2021)Discovery of Perception Performance Limiting Triggering Conditions in Automated Driving2021 5th International Conference on System Reliability and Safety (ICSRS)10.1109/ICSRS53853.2021.9660641(248-257)Online publication date: 24-Nov-2021
https://doi.org/10.1109/ICSRS53853.2021.9660641
Adee AGansch RLiggesmeyer P(2021)Systematic Modeling Approach for Environmental Perception Limitations in Automated Driving2021 17th European Dependable Computing Conference (EDCC)10.1109/EDCC53658.2021.00022(103-110)Online publication date: Sep-2021
https://doi.org/10.1109/EDCC53658.2021.00022
Jose SLouis SDascalu SLiu S(2020)Bayesian Network Structure Learning Using Case-Injected Genetic Algorithms2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00094(572-579)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00094
Zhang WFang WSun JChen Q(2020)Learning Bayesian Networks Structures with an Effective Knowledge-driven GA2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185884(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185884
Li HWang FLi H(2019)Integrating expert knowledge for Bayesian network structure learning based on intuitionistic fuzzy set and Genetic AlgorithmIntelligent Data Analysis10.3233/IDA-18387723:1(41-56)Online publication date: 20-Feb-2019
https://doi.org/10.3233/IDA-183877
Povéda GRegnier-Coudert OTeichteil-Königsbuch FDupont GArnold AGuerra JPicard MAuger AStützle T(2019)Evolutionary approaches to dynamic earth observation satellites mission planning under uncertaintyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321859(1302-1310)Online publication date: 13-Jul-2019
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Jose SLiu SLouis SDascalu S(2019)Towards a Hybrid Approach for Evolving Bayesian Networks Using Genetic Algorithms2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2019.00103(705-712)Online publication date: Nov-2019
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Contaldi CVafaee FNelson P(2019)Bayesian network hybrid learning using an elite-guided genetic algorithmArtificial Intelligence Review10.1007/s10462-018-9615-552:1(245-272)Online publication date: 1-Jun-2019
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Neau CRegnier-Coudert OMcCall J(2018)An Analysis of Indirect Optimisation Strategies for Scheduling2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477967(1-8)Online publication date: Jul-2018
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