A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm
Jaehun Lee, Wooyong Chung, Euntai Kim, Soohan Kim
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.
International Journal of Control, Automation and Systems
Lee, J., Chung, W., Kim, E., & Kim, S. (2010). A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm. International Journal of Control, Automation and Systems, 8(2), 398-407. https://doi.org/10.1007/s12555-010-0227-3
Lee, Jaehun ; Chung, Wooyong ; Kim, Euntai ; Kim, Soohan. / A new genetic approach for structure learning of Bayesian networks : Matrix genetic algorithm. In: International Journal of Control, Automation and Systems. 2010 ; Vol. 8, No. 2. pp. 398-407.
@article{987b995cda8548c1a674c117b18c7ff8,
title = "A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm",
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.",
author = "Jaehun Lee and Wooyong Chung and Euntai Kim and Soohan Kim",
journal = "International Journal of Control, Automation and Systems",
publisher = "Institute of Control, Robotics and Systems",
Lee, J, Chung, W, Kim, E & Kim, S 2010, 'A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm', International Journal of Control, Automation and Systems, vol. 8, no. 2, pp. 398-407. https://doi.org/10.1007/s12555-010-0227-3
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.
T1 - A new genetic approach for structure learning of Bayesian networks
T2 - Matrix genetic algorithm
AU - Lee, Jaehun
AU - Chung, Wooyong
AU - Kim, Euntai
AU - Kim, Soohan
N2 - 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.
AB - 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.
JO - International Journal of Control, Automation and Systems
JF - International Journal of Control, Automation and Systems
Lee J, Chung W, Kim E, Kim S. A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm. International Journal of Control, Automation and Systems. 2010 Apr 1;8(2):398-407. https://doi.org/10.1007/s12555-010-0227-3