17 May 2012 Immune allied genetic algorithm for Bayesian network structure learning
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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.
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Qin Song, Feng Lin, Wei Sun, KC Chang, "Immune allied genetic algorithm for Bayesian network structure learning", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839215 (17 May 2012); doi: 10.1117/12.920298; https://doi.org/10.1117/12.920298
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