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International Conference on Machine Learning, Optimization, and Data Science

LOD 2018: Machine Learning, Optimization, and Data Science pp 104–115Cite as

Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

In this paper, a clustering based surrogate is proposed to be used in offline data-driven multiobjective optimization to reduce the size of the optimization problem in the decision space. The surrogate is combined with an interactive multiobjective optimization approach and it is applied to forest management planning with promising results.

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Notes

  1. 1.

    Code available in https://github.com/josejuhani/gradu-code.

References

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Acknowledgment

This research was supported by the Academy of Finland (projects no. 311877 and 287496) and is related to the thematic research area DEMO of the University of Jyväskylä.

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Authors and Affiliations

  1. University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35 (Agora), FI-40014, University of Jyvaskyla, Finland

    Jussi Hakanen, Jose Malmberg & Vesa Ojalehto

  2. University of Jyvaskyla, Dept. of Biological and Environmental Science, P.O. Box 35, FI-40014, University of Jyvaskyla, Finland

    Kyle Eyvindson

Authors
  1. Jussi Hakanen
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  2. Jose Malmberg
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  3. Vesa Ojalehto
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  4. Kyle Eyvindson
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Corresponding author

Correspondence to Jussi Hakanen .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Hakanen, J., Malmberg, J., Ojalehto, V., Eyvindson, K. (2019). Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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