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Empirical Performance of the Approximation of the Least Hypervolume Contributor

  • Krzysztof Nowak
  • Marcus Märtens
  • Dario Izzo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)

Abstract

A fast computation of the hypervolume has become a crucial component for the quality assessment and the performance of modern multi-objective evolutionary optimization algorithms. Albeit recent improvements, exact computation becomes quickly infeasible if the optimization problems scale in their number of objectives or size. To overcome this issue, we investigate the potential of using approximation instead of exact computation by benchmarking the state of the art hypervolume algorithms for different geometries, dimensionality and number of points. Our experiments outline the threshold at which exact computation starts to become infeasible, but approximation still applies, highlighting the major factors that influence its performance.

Keywords

Hypervolume indicator performance indicators multi- objective optimization many-objective optimization approximation algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Krzysztof Nowak
    • 1
  • Marcus Märtens
    • 2
  • Dario Izzo
    • 1
  1. 1.European Space AgencyNoordwijkThe Netherlands
  2. 2.TU DelftDelftThe Netherlands

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