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Multi-objective Optimization of Problems with Epistemic Uncertainty

  • Conference paper
Evolutionary Multi-Criterion Optimization (EMO 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3410))

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Abstract

Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-world problems, uncertainty about the correctness of the system model and environmental factors does not allow to determine clear objective values. Stochastic sampling as applied in noisy EAs neglects that this so-called epistemic uncertainty is not an inherent property of the system and cannot be reduced by sampling methods. Therefore, some extensions for MOEAs to handle epistemic uncertainty in objective functions are proposed. The extensions are generic and applicable to most common MOEAs. A density measure for uncertain objectives is proposed to maintain diversity in the nondominated set. The approach is demonstrated to the reliability optimization problem, where uncertain component failure rates are usual and exhaustive tests are often not possible due to time and budget reasons.

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Department of Engineering, University of Duisburg-Essen, Bismarckstr. 90, 47057, Duisburg, Germany

    Philipp Limbourg

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  1. Philipp Limbourg
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Editors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), 07300, México, D.F., México

    Carlos A. Coello Coello

  2. Centro de Investigación en Matemáticas (CIMAT), A.P. 402, Guanajuato, 36000, Mexico

    Arturo Hernández Aguirre

  3. Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland

    Eckart Zitzler

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© 2005 Springer-Verlag Berlin Heidelberg

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Limbourg, P. (2005). Multi-objective Optimization of Problems with Epistemic Uncertainty. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_29

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  • DOI: https://doi.org/10.1007/978-3-540-31880-4_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24983-2

  • Online ISBN: 978-3-540-31880-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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