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Learning-Guided Exploration in Airfoil Optimization

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

A learning-based exploration approach is proposed to escape from the basins of attraction of converged-to optima, by selecting on what is termed the interestingness of a solution. This interestingness is based on the modeling error made by a surrogate model that is trained on all solutions encountered earlier during the search. Compared to multiple standard optimization runs, a learning-guided restart scheme that alternates between a quality optimization phase and an exploration phase directed by interestingness finds solutions that are more diverse and of higher quality.

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Author information

Authors and Affiliations

  1. Natural Computing Group, LIACS, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands

    Edgar Reehuis & Thomas Bäack

  2. Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, 63073, Offenbach/Main, Germany

    Edgar Reehuis, Markus Olhofer & Bernhard Sendhoff

Authors
  1. Edgar Reehuis
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  2. Markus Olhofer
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  3. Bernhard Sendhoff
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  4. Thomas Bäack
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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© 2013 Springer-Verlag Berlin Heidelberg

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Cite this paper

Reehuis, E., Olhofer, M., Sendhoff, B., Bäack, T. (2013). Learning-Guided Exploration in Airfoil Optimization. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_61

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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