MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization

  • Bernd Bischl
  • Simon Wessing
  • Nadja Bauer
  • Klaus Friedrichs
  • Claus Weihs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8426)

Abstract

The aim of this work is to compare different approaches for parallelization in model-based optimization. As another alternative aside from the existing methods, we propose using a multi-objective infill criterion that rewards both the diversity and the expected improvement of the proposed points. This criterion can be applied more universally than the existing ones because it has less requirements. Internally, an evolutionary algorithm is used to optimize this criterion. We verify the usefulness of the approach on a large set of established benchmark problems for black-box optimization. The experiments indicate that the new method’s performance is competitive with other batch techniques and single-step EGO.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bernd Bischl
    • 1
  • Simon Wessing
    • 2
  • Nadja Bauer
    • 1
  • Klaus Friedrichs
    • 1
  • Claus Weihs
    • 1
  1. 1.Department of StatisticsTU DortmundDortmundGermany
  2. 2.Department of Computer ScienceTU DortmundDortmundGermany

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