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Towards a New Evolutionary Computation pp 221–242Cite as

Bayesian Classifiers in Optimization: An EDA-like Approach

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 192))

Summary

This chapter introduces a new Evolutionary Computation method which applies Bayesian classifiers in the construction of a probabilistic graphical model that will be used to evolve a population of individuals. On the other hand, the satisfiability problem (SAT) is a central problem in the theory of computation as a representative example of NP-complete problems. We have verified the performance of this new method for the SAT problem. We compare three different solution representations suggested in the literature. Finally, we apply local search methods for this problem.

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Author information

Authors and Affiliations

  1. Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080, San Sebastian, Spain

    Teresa Miquélez & Endika Bengoetxea

  2. Department of Computer Science and Artificial Intelligence, University of the Basque Country, P.O. Box 649, 20080, San Sebastian, Spain

    Pedro Larrañaga

Authors
  1. Teresa Miquélez
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  2. Endika Bengoetxea
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  3. Pedro Larrañaga
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Apartado de correos 649, 20080, Donostia-San Sebastian, Spain

    Jose A. Lozano , Pedro Larrañaga  & Iñaki Inza ,  & 

  2. Intelligent Systems Group, Department of Architecture and Computer Technology, University of the Basque Country, 20080, Donostia-San Sebastián, Spain

    Endika Bengoetxea

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© 2006 Springer-Verlag Berlin Heidelberg

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Miquélez, T., Bengoetxea, E., Larrañaga, P. (2006). Bayesian Classifiers in Optimization: An EDA-like Approach. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds) Towards a New Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32494-1_9

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  • DOI: https://doi.org/10.1007/3-540-32494-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29006-3

  • Online ISBN: 978-3-540-32494-2

  • eBook Packages: EngineeringEngineering (R0)

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