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An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization

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Parallel Problem Solving from Nature – PPSN XIII (PPSN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8672))

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Abstract

This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set.

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References

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano, 380-8553, Japan

    Hernán Aguirre & Kiyoshi Tanaka

  2. Université Lille 1 LIFL, UMR CNRS 8022, Inria Lille-Nord Europe, France

    Arnaud Liefooghe

  3. LISIC, Université du Littoral Côte d’Opale, 62228, Calais, France

    Sébastien Verel

Authors
  1. Hernán Aguirre
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  2. Arnaud Liefooghe
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  3. Sébastien Verel
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  4. Kiyoshi Tanaka
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Editor information

Editors and Affiliations

  1. Faculty of Computer and Engineering Sciences, Cologne University of Applied Sciences, Steinmüllerallee 1, 51643, Gummersbach, Germany

    Thomas Bartz-Beielstein

  2. Warwick Business School, University of Warwick, CV8 2SY, Coventry, UK

    Jürgen Branke

  3. Department of Intelligent Systems, JožefStefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Bogdan Filipič

  4. Department of Computer Science and Creative Technologies, University of the West of England, BS16 1QY, Bristol, UK

    Jim Smith

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© 2014 Springer International Publishing Switzerland

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Aguirre, H., Liefooghe, A., Verel, S., Tanaka, K. (2014). An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_48

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10761-5

  • Online ISBN: 978-3-319-10762-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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