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Clustering-Based Selection for Evolutionary Many-Objective Optimization

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Book cover 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 paper discusses a selection scheme allowing to employ a clustering technique to guide the search in evolutionary many-objective optimization. The underlying idea to avoid the curse of dimensionality is based on transforming the objective vectors before applying a clustering and the selection of cluster representatives according to the distance to a reference point. The experimental results reveal that the proposed approach is able to effectively guide the search in high-dimensional objective spaces, producing highly competitive performance when compared with state-of-the-art algorithms.

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Author information

Authors and Affiliations

  1. Algoritmi R&D Center, University of Minho, Braga, Portugal

    Roman Denysiuk

  2. Department of Production and Systems Engineering, University of Minho, Braga, Portugal

    Lino Costa & Isabel Espírito Santo

  3. University of Minho, Braga, Portugal

    Roman Denysiuk, Lino Costa & Isabel Espírito Santo

Authors
  1. Roman Denysiuk
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  2. Lino Costa
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  3. Isabel Espírito Santo
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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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Denysiuk, R., Costa, L., Espírito Santo, I. (2014). Clustering-Based Selection for Evolutionary 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_53

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_53

  • 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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