Bayesian network structure learning using chaos hybrid genetic algorithm

Jiajie Shen; Feng Lin; Wei Sun; KC Chang

doi:10.1117/12.920302

17 May 2012 Bayesian network structure learning using chaos hybrid genetic algorithm

Jiajie Shen, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 839216 (2012) https://doi.org/10.1117/12.920302
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Jiajie Shen, Feng Lin, Wei Sun, and KC Chang "Bayesian network structure learning using chaos hybrid genetic algorithm", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839216 (17 May 2012); https://doi.org/10.1117/12.920302

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