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Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:310-316, 1995.
Abstract
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), GAe λ (Genetic Algorithm elistist of degree λ ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAe λ (hybrid Genetic Algorithm elitist of degree λ ) - to simulations of the ALARM Network. The behaviour of the mentioned algorithms is studied with respect to their parameters.