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Bayesian Optimization Algorithms for Dynamic Problems

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Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

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Abstract

This paper is an experimental study investigating the capability of Bayesian optimization algorithms to solve dynamic problems. We tested the performance of two variants of Bayesian optimization algorithms – Mixed continuous-discrete Bayesian Optimization Algorithm (MBOA), Adaptive Mixed Bayesian Optimization Algorithm (AMBOA) – and new proposed modifications with embedded Sentinels concept and Hypervariance. We have compared the performance of these variants on a simple dynamic problem – a time-varying function with predefined parameters. The experimental results confirmed the benefit of Sentinels concept and Hypervariance embedded into MBOA algorithm for tracking a moving optimum.

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References

  1. Branke, J.: Evolutionary Optimization in Dynamic Environment, University of Karlsruhe, Germany. Kluwer Academic Publishers, Dordrecht (2002)

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  5. Ocenasek, J., Kern, S., Hansen, N., Müller, S., Koumoutsakos, P.: A Mixed Bayesian Optimization Algorithm with Variance Adaptation. In: Parallel Problem Solving From Nature – PPSN VIII, pp. 352–361. Springer, Berlin (2004)

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Author information

Authors and Affiliations

  1. Department of Computer Systems, Brno University of Technology, Faculty of Information Technology, Božetěchova 2, Brno, 612 66, CZ

    Miloš Kobliha & Josef Schwarz

  2. Kimotion Technologies, Leuvensesteenweg 200, Boutersem, B-3370, Belgium

    Jiří Očenášek

Authors
  1. Miloš Kobliha
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  2. Josef Schwarz
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  3. Jiří Očenášek
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

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© 2006 Springer-Verlag Berlin Heidelberg

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Kobliha, M., Schwarz, J., Očenášek, J. (2006). Bayesian Optimization Algorithms for Dynamic Problems. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_77

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  • DOI: https://doi.org/10.1007/11732242_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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