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A Causal Framework for Understanding Optimisation Algorithms

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Trustworthy AI - Integrating Learning, Optimization and Reasoning (TAILOR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12641))

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Abstract

Over the last decades, plenty of exact and non-exact methods have been proposed to tackle NP-hard optimisation problems.

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References

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles (ULB), Brussels, Belgium

    Alberto Franzin & Thomas Stützle

Authors
  1. Alberto Franzin
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  2. Thomas Stützle
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Corresponding author

Correspondence to Alberto Franzin .

Editor information

Editors and Affiliations

  1. Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Fredrik Heintz

  2. ALMA-AI Research Institute on Human-Centered AI, University of Bologna, Bologna, Italy

    Michela Milano

  3. Department of Computer Science, University College Cork, Cork, Ireland

    Barry O'Sullivan

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Franzin, A., Stützle, T. (2021). A Causal Framework for Understanding Optimisation Algorithms. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-73959-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73958-4

  • Online ISBN: 978-3-030-73959-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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