Immune Allied Genetic Algorithm for Bayesian Network Structure Learning

被引：0

作者：

Song, Qin ^{[1
]}

Lin, Feng ^{[1
]}

Sun, Wei ^{[2
]}

Chang, K. C. ^{[2
]}

机构：

[1] Zhejiang Univ, Sch Elect Engn, 38 Yugu Rd, Hangzhou 310027, Zhejiang, Peoples R China

[2] George Mason Univ, Ctr Excellence C4I, Fairfax, VA 22030 USA

来源：

SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XXI | 2012年 / 8392卷

关键词：

Bayesian network; structure learning; the allied GA; immune theory;

D O I：

10.1117/12.920298

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

引用

页数：10

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