Immune allied genetic algorithm for Bayesian network structure learning

Qin Song; Feng Lin; Wei Sun; KC Chang

doi:10.1117/12.920298

17 May 2012 Immune allied genetic algorithm for Bayesian network structure learning

Qin Song, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 839215 (2012) https://doi.org/10.1117/12.920298
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Qin Song, Feng Lin, Wei Sun, and 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); https://doi.org/10.1117/12.920298

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