A Matrix-Based Genetic Algorithm for Structure Learning of Bayesian Networks
- Authors
- 고송; 김대원; 강보영
- Issue Date
- Sep-2011
- Publisher
- 한국지능시스템학회
- Keywords
- Bayesian Network; Genetic Algorithm; Structure Learning; Genetic Operators.
- Citation
- International Journal of Fuzzy Logic and Intelligent systems, v.11, no.3, pp 135 - 142
- Pages
- 8
- Journal Title
- International Journal of Fuzzy Logic and Intelligent systems
- Volume
- 11
- Number
- 3
- Start Page
- 135
- End Page
- 142
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/27485
- ISSN
- 1598-2645
- Abstract
- Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method,we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.
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