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Proceedings Paper

Bayesian network structure learning using chaos hybrid genetic algorithm
Author(s): Jiajie Shen; Feng Lin; Wei Sun; KC Chang
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Paper Abstract

A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

Paper Details

Date Published: 17 May 2012
PDF: 12 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839216 (17 May 2012); doi: 10.1117/12.920302
Show Author Affiliations
Jiajie Shen, 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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