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A Particle Swarm Optimization and Immune Theory-Based Algorithm for Structure Learning of Bayesian Networks

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Executive Summary

Bayesian network is a directed acyclic graph. Existing Bayesian network learning approaches based on search & scoring usually work with a heuristic search for finding the highest scoring structure. This paper describes a new data mining algorithm to learn Bayesian networks structures based on an Immune Binary Particle Swarm Optimization (IB- PSO) method and the Minimum Description Length (MDL) principle. IBPSO is proposed by combining the immune theory in biology with Particle Swarm Optimization (PSO). It constructs an immune operator accomplished by two steps, vaccination and immune selection. The purpose of adding immune operator is to prevent and overcome premature convergence. Experiments show that IBPSO not only improves the quality of the solutions, but also reduces the time cost.

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