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Proceedings Paper

Immune allied genetic algorithm for Bayesian network structure learning
Author(s): Qin Song; Feng Lin; Wei Sun; KC Chang
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Paper Abstract

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.

Paper Details

Date Published: 17 May 2012
PDF: 10 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839215 (17 May 2012); doi: 10.1117/12.920298
Show Author Affiliations
Qin Song, Zhejiang Univ. (China)
Feng Lin, Zhejiang Univ. (China)
Wei Sun, George Mason Univ. (United States)
KC Chang, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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