Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

This paper demonstrates how genetic algorithms can be used to discover the structure of a Bayesian network from a given database with cases. The results presented, were obtained by applying four different types of genetic algorithms — SSGA (Steady State Genetic Algorithm), GAeλ (Genetic Algorithm elistist of degree λ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAeλ (hybrid Genetic Algorithm elitist of degree λ) — to simulations of the ALARM Network. The behaviour of these algorithms is studied as their parameters are varied.

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