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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2019: Evolutionary Multi-Criterion Optimization pp 151–164Cite as

Process-Monitoring-for-Quality—A Model Selection Criterion for Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11411))

Abstract

Process Monitoring for Quality is a manufacturing quality philosophy aimed at defect detection through binary classification that is founded on big data and big models. Genetic Programming (GP) algorithms have been successfully applied by following the big models learning paradigm for rare quality event detection (classification). Since it is a bias-free technique unmarred by human preconceptions, it can potentially generate better solutions (models) compared with the best human efforts. However, since GP uses random search methods based on Darwinian philosophy of “survival of the fittest”, hundreds, or even thousands of models need to be created to find a good solution. In this context, model selection becomes a critical step in the process of finding the final model to be deployed at the plant. A three-objective optimization model selection criterion (\(3D-GP\)) is introduced for analyzing highly/ultra unbalanced data structures. It uses three competing attributes – prediction, separability, complexity – to project candidate models into a three-dimensional space to select the final model that solves the posed tradeoff between them the best.

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Author information

Authors and Affiliations

  1. General Motors, Research and Development, Warren, MI, 48092, USA

    Carlos A. Escobar & Diana M. Wegner

  2. Michigan State University, East Lansing, MI, 48823, USA

    Abhinav Gaur

  3. Tecnológico de Monterrey, 64849, Monterrey, NL, Mexico

    Carlos A. Escobar & Ruben Morales-Menendez

Authors
  1. Carlos A. Escobar
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  2. Diana M. Wegner
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  3. Abhinav Gaur
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  4. Ruben Morales-Menendez
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Corresponding author

Correspondence to Carlos A. Escobar .

Editor information

Editors and Affiliations

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

    Kalyanmoy Deb

  2. Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  4. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  5. University of Jyvaskyla, Jyväskylä, Finland

    Kaisa Miettinen

  6. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sanaz Mostaghim

  7. Cornell University, Ithaca, NY, USA

    Patrick Reed

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Escobar, C.A., Wegner, D.M., Gaur, A., Morales-Menendez, R. (2019). Process-Monitoring-for-Quality—A Model Selection Criterion for Genetic Programming. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12597-4

  • Online ISBN: 978-3-030-12598-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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