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 journalArticle

9 Citations (Scopus)

Abstract

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
Volume8
Issue number2
DOIs
Publication statusPublished - 2010 Apr 1

Fingerprint

Bayesian networks
Genetic algorithms
Chromosomes
Mathematical operators
Computer simulation

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

Cite this

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A new genetic approach for structure learning of Bayesian networks : Matrix genetic algorithm. / Lee, Jaehun; Chung, Wooyong; Kim, Euntai; Kim, Soohan.

In: International Journal of Control, Automation and Systems, Vol. 8, No. 2, 01.04.2010, p. 398-407.

Research output: Contribution to journalArticle

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