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.
|Number of pages
|International Journal of Control, Automation and Systems
|Published - 2010 Apr
Bibliographical noteFunding 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