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Enhancing Parameter Estimation of Biochemical Networks by Exponentially Scaled Search Steps

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Book cover Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4973))

Abstract

A fundamental problem of modelling in Systems Biology is to precisely characterise quantitative parameters, which are hard to measure experimentally. For this reason, it is common practise to estimate these parameter values, using evolutionary and other techniques, by fitting the model behaviour to given data. In this contribution, we extensively investigate the influence of exponentially scaled search steps on the performance of two evolutionary and one deterministic technique; namely CMA-Evolution Strategy, Differential Evolution, and the Hooke-Jeeves algorithm, respectively. We find that in most test cases, exponential scaling of search steps significantly improves the search performance for all three methods.

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Authors and Affiliations

  1. Bio Systems Analysis Group, Jena Centre for Bioinformatics and Friedrich Schiller University Jena, Ernst-Abbe-Platz 1–4, D-07743, Jena, Germany

    Hendrik Rohn, Bashar Ibrahim, Thorsten Lenser, Thomas Hinze & Peter Dittrich

Authors
  1. Hendrik Rohn
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  2. Bashar Ibrahim
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  3. Thorsten Lenser
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  4. Thomas Hinze
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  5. Peter Dittrich
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Elena Marchiori Jason H. Moore

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© 2008 Springer-Verlag Berlin Heidelberg

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Rohn, H., Ibrahim, B., Lenser, T., Hinze, T., Dittrich, P. (2008). Enhancing Parameter Estimation of Biochemical Networks by Exponentially Scaled Search Steps. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_16

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  • DOI: https://doi.org/10.1007/978-3-540-78757-0_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78756-3

  • Online ISBN: 978-3-540-78757-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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