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Evolving Generalized Solutions for Robust Multi-objective Optimization: Transportation Analysis in Disaster

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Evolutionary Multi-Criterion Optimization (EMO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11411))

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Abstract

This paper proposes the multi-objective evolutionary algorithm (MOEA) that can evolve the generalized individuals, which include many solutions that can be applied into different situations with the minimal change. The intensive simulations on the waterbus route optimization problem as the real world problem have revealed the following implications: (1) the proposed MOEA cannot only optimize the solutions like general MOEAs but also can evolve the generalized individuals; and (2) the proposed MOEA can analyze the feature of the river transportation in the waterbus route optimization.

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Author information

Authors and Affiliations

  1. The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo, Japan

    Keiki Takadama & Hiroyuki Sato

  2. National Maritime Research Institute, 6-38-1, Shinkawa, Mitaka, Tokyo, Japan

    Keiji Sato

Authors
  1. Keiki Takadama
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  2. Keiji Sato
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  3. Hiroyuki Sato
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Corresponding author

Correspondence to Keiki Takadama .

Editor information

Editors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Kalyanmoy Deb

  2. Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  4. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  5. University of Jyvaskyla, Jyväskylä, Finland

    Kaisa Miettinen

  6. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sanaz Mostaghim

  7. Cornell University, Ithaca, NY, USA

    Patrick Reed

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Takadama, K., Sato, K., Sato, H. (2019). Evolving Generalized Solutions for Robust Multi-objective Optimization: Transportation Analysis in Disaster. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_39

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12597-4

  • Online ISBN: 978-3-030-12598-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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