Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

  • Pedro Larrañaga
  • Roberto Murga
  • Mikel Poza
  • Cindy Kuijpers
Part of the Lecture Notes in Statistics book series (LNS, volume 112)

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.

Keywords

Lution 

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Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • Pedro Larrañaga
    • 1
  • Roberto Murga
    • 1
  • Mikel Poza
    • 1
  • Cindy Kuijpers
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of the Basque CountrySan SebastiánSpain

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