On the Use of a Non-redundant Encoding for Learning Bayesian Networks from Data with a GA

  • Steven van Dijk
  • Dirk Thierens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

We study the impact of the choice of search space for a GA that learns Bayesian networks from data. The most convenient search space is redundant and therefore allows for multiple representations of the same solution and possibly disruption during crossover. An alternative search space eliminates this redundancy, and potentially allows a more efficient search to be conducted. On the other hand, a non-redundant encoding requires a more complicated implementation. We experimentally compare several plausible approaches (GAs) to study the impact of this and other design decisions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Steven van Dijk
    • 1
  • Dirk Thierens
    • 1
  1. 1.Institute of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands

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