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Concepts for the Analysis of the ES

  • Hans-Georg Beyer
Part of the Natural Computing Series book series (NCS)

Abstract

In this chapter the theoretical framework will be defined in which the analysis of the ES will be conducted in the following chapters. In particular, the progress measures will be defined here, which can be used in the evaluation of the optimization performance of an EA (i.e. not only ES). These measures describe the local and microscopic behavior of an EA. The macroscopic behavior (i.e. the dynamics of ES) is handled in Sect. 2.4. The calculation of the progress measures requires the selection of appropriate fitness landscapes, which can be used as the models of the real landscapes. Sect. 2.2 is devoted to this topic. The differential-geometric model — which can be used for the approximation of the local (differentiable) fitness landscapes — is introduced in Sect. 2.3.

Keywords

Sphere Model Progress Rate Fitness Landscape Linear Convergence Progress Measure 
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 2001

Authors and Affiliations

  • Hans-Georg Beyer
    • 1
  1. 1.Department of Computer ScienceUniversity of DortmundDortmundGermany

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