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Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms

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Evolutionary Multi-Criterion Optimization (EMO 2017)

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Abstract

Evolutionary algorithms allow for solving a wide range of multi-objective optimization problems. Nevertheless for complex practical problems, including domain knowledge is imperative to achieve good results. In many domains, single-objective expert knowledge is available, but its integration into modern multi-objective evolutionary algorithms (MOEAs) is often not trivial and infeasible for practitioners. In addition to the need of modifying algorithm architectures, the challenge of combining single-objective knowledge to multi-objective rules arises. This contribution takes a step towards a multi-objective optimization framework with defined interfaces for expert knowledge integration. Therefore, multi-objective mutation and local search operators are integrated into the two MOEAs MOEA/D and R-NSGAII. Results from experiments on exemplary machine scheduling problems prove the potential of such a concept and motivate further research in this direction.

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Notes

  1. 1.

    Note, however, that we focus on the more general context of applicability in MOEAs. Thus, the proposed methodology is not restricted to specific applications but strive for identifying additional integration points of expertise.

  2. 2.

    This configuration was applied without fine tuning to demonstrate the methodology. Future rigorous investigation may consider systematically generated configurations.

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Authors and Affiliations

  1. RWTH Aachen University, Kackertstr. 7, 52072, Aachen, Germany

    Magdalena A. K. Lang

  2. Westfälische Wilhelms-Universität Münster, Leonardo-Campus 3, 48149, Münster, Germany

    Christian Grimme

Authors
  1. Magdalena A. K. Lang
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  2. Christian Grimme
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Corresponding author

Correspondence to Christian Grimme .

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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Lang, M.A.K., Grimme, C. (2017). Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms. 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_26

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_26

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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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