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On Quality Indicators for Black-Box Level Set Approximation

  • Michael T. M. Emmerich
  • André H. Deutz
  • Johannes W. Kruisselbrink
Part of the Studies in Computational Intelligence book series (SCI, volume 447)

Abstract

This chapter reviews indicators that can be used to compute the quality of approximations to level sets for black-box functions. Such problems occur, for instance, when finding sets of solutions to optimization problems or in solving nonlinear equation systems. After defining and motivating level set problems from a decision theoretic perspective, we discuss quality indicators that could be used to measure how well a set of points approximates a level set. We review simple indicators based on distance, indicators from biodiversity, and propose novel indicators based on the concept of Hausdorff distance. We study properties of these indicators with respect to continuity, spread, and monotonicity and also discuss computational complexity. Moreover, we study the use of these indicators in a simple indicatorbased evolutionary algorithm for level set approximation.

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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Michael T. M. Emmerich
    • 1
  • André H. Deutz
    • 1
  • Johannes W. Kruisselbrink
    • 1
  1. 1.LIACSLeidenThe Netherlands

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