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Probability of Perfect Reconstruction of Pareto Set in Multi-Objective Optimization

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Neural Information Processing (ICONIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8227))

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Abstract

We proposed a method for collecting all Pareto solutions in multi-objective optimization problems. Our method is similar to randomized algorithms or the statistical learning theory and completely reconstructs the Pareto set with high probability from a finite number of single-objective optimizations. We also derived an upper bound of the mean of the probability that the method fails the perfect reconstruction. Our analysis shows the mean error probability decreases as the number of single-objective optimizations increases.

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Author information

Authors and Affiliations

  1. Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan

    Kazushi Ikeda & Akira Hontani

Authors
  1. Kazushi Ikeda
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  2. Akira Hontani
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Editor information

Editors and Affiliations

  1. Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  2. The University of Tokyo, 7-3-1 Hongo, 113-8656, Bunkyo-ku, Tokyo, Japan

    Akira Hirose

  3. Institute of Automation, Key Laboratory of Complex Systems and Intelligence Science, Chinese Academy of Sciences, 100190, Beijing, China

    Zeng-Guang Hou

  4. Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, 440-746, Suwon, Korea

    Rhee Man Kil

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© 2013 Springer-Verlag Berlin Heidelberg

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Ikeda, K., Hontani, A. (2013). Probability of Perfect Reconstruction of Pareto Set in Multi-Objective Optimization. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-42042-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42041-2

  • Online ISBN: 978-3-642-42042-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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