
Proceedings Paper
Immune allied genetic algorithm for Bayesian network structure learningFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)
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
Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)
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