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Global Search through Sampling Using a PDF

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Stochastic Algorithms: Foundations and Applications (SAGA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2827))

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Abstract

This paper presents a direct search algorithm called PGSL – Probabilistic Global Search Lausanne. PGSL performs global search by sampling the solutions space using a probability density function (PDF). The PDF is updated in four nested cycles such that the search focuses on regions containing good solutions without avoiding other regions altogether. Tests on benchmark problems having multi-parameter non-linear objective functions revealed that PGSL performs better than genetic algorithms in most cases that were studied. Furthermore as problem sizes increase, PGSL performs increasingly better than other approaches. Finally, PGSL has already proved to be valuable for engineering tasks in areas of design, diagnosis and control.

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Authors and Affiliations

  1. EPFL-Federal Institute of Technology, IMAC, CH-1015, Lausanne, Switzerland

    Benny Raphael & Ian F. C. Smith

Authors
  1. Benny Raphael
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  2. Ian F. C. Smith
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Editor information

Editors and Affiliations

  1. Science and Technology Research Institute, University of Hertfordshire, AL10 9AB, Hatfield, Herts, UK

    Andreas Albrecht

  2. Department of Computer Science, King’s College London, WC2R 2LS, London, UK

    Kathleen Steinhöfel

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© 2003 Springer-Verlag Berlin Heidelberg

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Raphael, B., Smith, I.F.C. (2003). Global Search through Sampling Using a PDF. In: Albrecht, A., Steinhöfel, K. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2003. Lecture Notes in Computer Science, vol 2827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39816-5_7

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  • DOI: https://doi.org/10.1007/978-3-540-39816-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20103-8

  • Online ISBN: 978-3-540-39816-5

  • eBook Packages: Springer Book Archive

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