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Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results

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Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

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Abstract

In many practical optimization problems, evaluation of objectives and constraints often involve computationally expensive procedures. To handle such problems, a metamodel-assisted approach is usually used to complete an optimization run in a reasonable amount of time. A metamodel is an approximate mathematical model of an objective or a constrained function which is constructed with a handful of solutions evaluated exactly. However, when comes to solving multi-objective optimization problems involving numerous constraints, it may be too much to metamodel each and every objective and constrained function independently. The cumulative effect of errors from each metamodel may turn out to be detrimental for the accuracy of the overall optimization procedure. In this paper, we propose a taxonomy of various metamodeling methodologies for multi-objective optimization and provide a comparative study by discussing advantages and disadvantages of each method. The first results presented in this paper are obtained using the well-known Kriging metamodeling approach. Based on our proposed taxonomy and an extensive literature search, we also highlight new and promising methods for multi-objective metamodeling algorithms.

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Acknowledgment

Authors acknowledge the Matlab Kriging code provided by Dr. Cem Tutum.

Author information

Authors and Affiliations

  1. Michigan State University, East Lansing, Michigan, 48824, USA

    Kalyanmoy Deb, Rayan Hussein & Proteek Roy

  2. Information Technology Laboratory, CINVESTAV-Tamaulipas, 87130, Victoria, Tamps, Mexico

    Gregorio Toscano

Authors
  1. Kalyanmoy Deb
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  2. Rayan Hussein
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  3. Proteek Roy
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  4. Gregorio Toscano
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Corresponding author

Correspondence to Kalyanmoy Deb .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Deb, K., Hussein, R., Roy, P., Toscano, G. (2017). Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54156-3

  • Online ISBN: 978-3-319-54157-0

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