Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

P. Larrañaga, R.H. Murga, M. Poza, Cindy Kuijpers

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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), 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.
Original languageEnglish
Title of host publicationLearning from Data
Subtitle of host publicationArtificial Intelligence and Statistics V
EditorsD. Fisher, H. Lenz
Place of PublicationNew York
PublisherSpringer
Pages165-174
Publication statusPublished - 1996
Externally publishedYes

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