A hybrid approach to learn Bayesian networks using evolutionary programming

Man Leung WONG; Shing Yan LEE; Kwong Sak LEUNG

doi:10.1109/CEC.2002.1004433

A hybrid approach to learn Bayesian networks using evolutionary programming

Man Leung WONG, Shing Yan LEE, Kwong Sak LEUNG

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › peer-review

7 Citations (Scopus)

Abstract

A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.

Original language	English
Title of host publication	Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
Publisher	IEEE Computer Society
Pages	1314-1319
Number of pages	6
Volume	2
ISBN (Print)	9780780372825
DOIs	https://doi.org/10.1109/CEC.2002.1004433
Publication status	Published - 1 Jan 2002

Bibliographical note

Paper presented at the IEEE World Congress on Computational Intelligence (WCCI2002), May 12-17, 2002, Honolulu, Hawaii. ISBN of the source publication: 9780780372825

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10.1109/CEC.2002.1004433

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title = "A hybrid approach to learn Bayesian networks using evolutionary programming",

abstract = "A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.",

author = "WONG, {Man Leung} and LEE, {Shing Yan} and LEUNG, {Kwong Sak}",

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