research-article

Bayesian network structure learning using cooperative coevolution

Authors:

Olivier Barriàre,

Evelyne Lutton,

Pierre-Henri WuilleminAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 755 - 762

https://doi.org/10.1145/1569901.1570006

Published: 08 July 2009 Publication History

Abstract

We propose a cooperative-coevolution - Parisian trend - algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It is based on an intermediate stage which consists of evaluating an independence model of the data to be modelled. The Parisian cooperative coevolution is particularly well suited to the structure of this intermediate problem, and allows to represent an independence model with help of a whole population, each individual being an independence statement, i.e. a component of the independence model. Once an independence model is estimated, a Bayesian network can be built. This two level resolution of the complex problem of Bayesian network structure estimation has the major advantage to avoid the difficult problem of direct acyclic graph representation within an evolutionary algorithm, which causes many troubles related to constraints handling and slows down algorithms. Comparative results with a deterministic algorithm, PC, on two test cases (including the Insurance BN benchmark), prove the efficiency of IMPEA, which provides better results than PC in a comparable computation time, and which is able to tackle more complex issues than PC.

References

[1]

]]C. Baudrit, P. Wuillemin, M. Sicard, and N. Perrot. A Dynamic Bayesian Network to Represent a Ripening Process of a Soft Mould Cheese. In KES '08: 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II pages 265--272. Springer-Verlag, 2008.

Digital Library

[2]

]]J. Binder, D. Koller, S. Russell, and K. Kanazawa. Adaptive probabilistic networks with hidden variables. Machine Learning 29:213--244, 1997.

Digital Library

[3]

]]J. Cheng, D. A. Bell, and W. Liu. Learning Belief Networks from Data:An Information Theory Based Approach. In Sixth ACM International Conference on Information and Knowledge Management pages 325--331, 1997.

Digital Library

[4]

]]D. M. Chickering and C. Boutilier. Learning equivalence classes of Bayesian-network structures. In Journal of Machine Learning Research pages 150--157. Morgan Kaufmann, 1996.

Digital Library

[5]

]]D. M. Chickering, D. Heckerman, and C. Meek. Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research 5:1287--1330, 2004.

Digital Library

[6]

]]C. Chow and C. Liu. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14(3):462--467, 1968.

Digital Library

[7]

]]G. F. CooperandE. Herskovits. ABayesianMethod for the Induction of Probabilistic Networks from Data. Machine Learning 09(4):309--347, 1992.

Digital Library

[8]

]]K. Deb and D. Goldberg. An investigation of niche and species formation in genetic function optimization. In Third international conference on Genetic algorithms pages 42--50. Morgan Kaufmann Publishers Inc., 1989.

Digital Library

[9]

]]O. Francois and P. Leray. Etude comparative d 'algorithmes d 'apprentissage de structure dans les réseaux Bayésiens. In Rencontres des Jeunes Chercheurs en IA 2003.

[10]

]]N. Friedman. Learning Belief Networks in the Presence of Missing Values and Hidden Variables. In 14th International Conference on Machine Learning pages 125--133. Morgan Kaufmann, 1997.

Digital Library

[11]

]]N. Friedman, M. Linial, I. Nachman, and D. Pe 'er. Using Bayesian Network to Analyze Expression Data. J. Computational Biology 7:601--620, 2000.

[12]

]]D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In Second International Conference on Genetic Algorithms and their application pages 41--49. Lawrence Erlbaum Associates, Inc., 1987.

Digital Library

[13]

]]H. Jia, D. Liu, and P. Yu. Learning dynamic Bayesian network with immune evolutionary algorithm. 2005

[14]

]]P. Larrañaga and M. Poza. Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Journal on Pattern Analysis and Machine Intelligence 18(9):912--926, 1996.

Digital Library

[15]

]]J. Louchet, M. Guyon, M. J. Lesot, and A. M. Boumaza. Dynamic flies: a new pattern recognition tool applied to stereo sequence processing. Pattern Recognition Letters 23(1-3):335--345, 2002.

Digital Library

[16]

]]E. Lutton and P. Martinez. A Genetic Algorithm with Sharing for the Detection of 2D Geometric Primitives in Images. In AE '95: Selected Papers from the European conference on Artificial Evolution pages 287--303. Springer-Verlag, 1995

Digital Library

[17]

]]K. Murphy. The Bayes Net Toolbox for Matlab. Computing Science and Statistics 33(2):1024--1034, 2001.

[18]

]]J. W. Myers, K. B. Laskey, and K. A. DeJong. Learning Bayesian Networks from Incomplete Data using Evolutionary Algorithms. In Genetic and Evolutionary Computation Conference volume1, pages 458--465. Morgan Kaufmann, 1999.

[19]

]]G. Ochoa, E. Lutton, and E. Burke. The Cooperative Royal Road:Avoiding Hitchhiking. In Artificial Evolution pages 184--195, 2007.

Digital Library

[20]

]]J. Pearl and T. Verma. A theory of inferred causation. In Second International Conference on the Principles of Knowledge Representation and Reasoning 1991.

Digital Library

[21]

]]R. W. Robinson. Counting unlabeled acyclic digraphs. Combinatorial mathematics V 622:28--43, 1977.

[22]

]]B. J. Ross and E. Zuviria. Evolving dynamic Bayesian networks with Multi-objective genetic algorithms. Applied Intelligence 26, 2007.

Digital Library

[23]

]]P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search The MIT Press, second edition, 2001.

[24]

]]A. Tucker and X. Liu. Extending Evolutionary Programming Methods to the Learning of Dynamic Bayesian Networks. In GECCO '99 1999.

[25]

]]S. C. Wang and S. P. Li. Learning Bayesian Networks by Lamarckian Genetic Algorithm and Its Application to Yeast Cell-Cycle Gene Network Reconstruction from Time-Series Microarray Data. 3141/2004:49--62.

[26]

]]M. L. Wong and K. S. Leung. An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach. 8:378--404, 2004.

Digital Library

Cited By

Liu ELi JKinnebrew GZhang PZhang YCheng LLi L(2021)A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory NetworksIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2019.294482618:4(1325-1335)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TCBB.2019.2944826
Gray CAl-Maliki SVidal F(2018)Data exploration in evolutionary reconstruction of PET imagesGenetic Programming and Evolvable Machines10.1007/s10710-018-9330-719:3(391-419)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10710-018-9330-7
LI GXING LZHANG ZCHEN Y(2017)A New Bayesian Network Structure Learning Algorithm Mechanism Based on the Decomposability of Scoring FunctionsIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E100.A.1541E100.A:7(1541-1551)Online publication date: 2017
https://doi.org/10.1587/transfun.E100.A.1541
Show More Cited By

Index Terms

Bayesian network structure learning using cooperative coevolution

Recommendations

Avoiding convergence in cooperative coevolution with novelty search
AAMAS '14: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems

Cooperative coevolution is an approach for evolving solutions composed of coadapted components. Previous research has shown, however, that cooperative coevolutionary algorithms are biased towards stability: they tend to converge prematurely to ...
Novelty-driven cooperative coevolution

Cooperative coevolutionary algorithms CCEAs rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous ...
Pareto cooperative coevolutionary genetic algorithm using reference sharing collaboration
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Epistasis has been a well-known hard problem in optimization solved by evolution, especially by cooperative coevolution. Standard cooperative coevolution usually gets worse performance than standard evolution for optimization problems with epistasis. In ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO09

Sponsor:

GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
295
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu ELi JKinnebrew GZhang PZhang YCheng LLi L(2021)A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory NetworksIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2019.294482618:4(1325-1335)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TCBB.2019.2944826
Gray CAl-Maliki SVidal F(2018)Data exploration in evolutionary reconstruction of PET imagesGenetic Programming and Evolvable Machines10.1007/s10710-018-9330-719:3(391-419)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10710-018-9330-7
LI GXING LZHANG ZCHEN Y(2017)A New Bayesian Network Structure Learning Algorithm Mechanism Based on the Decomposability of Scoring FunctionsIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E100.A.1541E100.A:7(1541-1551)Online publication date: 2017
https://doi.org/10.1587/transfun.E100.A.1541
Choi SCho S(2017)Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary ComputationInternational Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding10.1007/978-3-319-67180-2_1(3-12)Online publication date: 23-Aug-2017
https://doi.org/10.1007/978-3-319-67180-2_1
Lutton EPerrot NTonda A(2016)BibliographyEvolutionary Algorithms for Food Science and Technology10.1002/9781119136828.biblio(127-148)Online publication date: 26-Nov-2016
https://doi.org/10.1002/9781119136828.biblio
Li GXing LChen Y(2015)A New BN Structure Learning Mechanism Based on Decomposability of Scoring FunctionsBio-Inspired Computing -- Theories and Applications10.1007/978-3-662-49014-3_19(212-224)Online publication date: 24-Dec-2015
https://doi.org/10.1007/978-3-662-49014-3_19
Tonda ASpritzer ALutton E(2014)Balancing User Interaction and Control in BNSLArtificial Evolution10.1007/978-3-319-11683-9_17(211-223)Online publication date: 25-Oct-2014
https://doi.org/10.1007/978-3-319-11683-9_17
Fukuda SYoshihiro T(2014)Learning Bayesian Networks Using Probability VectorsDistributed Computing and Artificial Intelligence, 11th International Conference10.1007/978-3-319-07593-8_58(503-510)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07593-8_58
LarrañAga PKarshenas HBielza CSantana R(2013)A review on evolutionary algorithms in Bayesian network learning and inference tasksInformation Sciences: an International Journal10.1016/j.ins.2012.12.051233(109-125)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.12.051
Tonda ALutton ESquillero GWuillemin P(2013)A memetic approach to bayesian network structure learningProceedings of the 16th European conference on Applications of Evolutionary Computation10.1007/978-3-642-37192-9_11(102-111)Online publication date: 3-Apr-2013
https://dl.acm.org/doi/10.1007/978-3-642-37192-9_11
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten