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Encapsulated Evolution strategies for the determination of group contribution model parameters in order to predict thermodynamic properties

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

The computation of parameters for group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem. The model parameters are calculated using a regression method and applying certain error criteria. A complex objective function occurs for which an optimization algorithm has to find the global minimum. For simple increment or group contribution models it is often sufficient to use deterministically working optimization algorithms. However, if the model contains parameters in complex terms such as sums of exponential expressions, the optimization problem will be a non-linear regression problem and the search of the global optimum becomes rather difficult. In this paper we report, that conventional multimembered (Μ,λ)- and (Μ+λ.)-Evolution Strategies could not cope with such non-linear regression problems without further ado, whereas multimembered encapsulated Evolution Strategies with multi-dimensional step length control are better suited for the optimization problem considered here.

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Authors and Affiliations

  1. Institute for Thermodynamics, 44221, Dortmund, Germany

    Hannes Geyer, Peter Ulbig & Siegfried Schulz

  2. Department of Chemical Engineering, University of Dortmund Member of the Collaborative Research Center, SFB 531, Germany

    Hannes Geyer, Peter Ulbig & Siegfried Schulz

Authors
  1. Hannes Geyer
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  2. Peter Ulbig
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  3. Siegfried Schulz
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Geyer, H., Ulbig, P., Schulz, S. (1998). Encapsulated Evolution strategies for the determination of group contribution model parameters in order to predict thermodynamic properties. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056939

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  • DOI: https://doi.org/10.1007/BFb0056939

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

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