A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm

Jaehun Lee, Wooyong Chung, Euntai Kim, Soohan Kim

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote the connection in the BN structure. Further, new genetic operators are developed to implement the MGA. The genetic operators are closed in the set of the directed acyclic graph (DAG). Finally, the proposed scheme is applied to real world and benchmark applications, and its effectiveness is demonstrated through computer simulation.

Original languageEnglish
Pages (from-to)398-407
Number of pages10
JournalInternational Journal of Control, Automation and Systems
Issue number2
Publication statusPublished - 2010 Apr

Bibliographical note

Funding Information:
__________ Manuscript received November 20, 2008; accepted September 27, 2009. Recommended by Editorial Board member Sungshin Kim under the direction of Editor Young-Hoon Joo. This work was supported by the Ministry of Commerce, Industry and Energy of Korea (HISP). E. Kim appreciates the financial support from LG Yonam Foundation during his sabbatical year at the University of California, Berkeley.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications


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