Convergence

Bayesian network structure learning based on the chaotic particle swarm optimization algorithm

Q. Zhang, Li, Z., Zhou, C. J., and Wei, X. P., Bayesian network structure learning based on the chaotic particle swarm optimization algorithm, vol. 12, pp. 4468-4479, 2013.

The Bayesian network (BN) is a knowledge representa­tion form, which has been proven to be valuable in the gene regulatory network reconstruction because of its capability of capturing causal re­lationships between genes. Learning BN structures from a database is a nondeterministic polynomial time (NP)-hard problem that remains one of the most exciting challenges in machine learning. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, the classical K2 algorithm is the most suc­cessful.

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