Optimization with noisy function evaluations

  • Volker Nissen
  • Jörn Propach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


In the optimization literature it is frequently assumed that the quality of solutions can be determined by calculating deterministic objective function values. Practical optimization problems, however, often require the evaluation of solutions through experimentation, stochastic simulation, sampling, or even interaction with the user. Thus, most practical problems involve noise. We empirically investigate the robustness of population-based versus point-based optimization methods on a range of parameter optimization problems when noise is added. Our results favor population-based optimization, and the evolution strategy in particular.


Genetic Algorithm Pattern Search Sphere Function Deterministic Case Threshold Accept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Volker Nissen
    • 1
  • Jörn Propach
    • 2
  1. 1.IDS Prof. Scheer GmbHSaarbrücken
  2. 2.Universität GöttingenGöttingen

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