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Nearest-Better-Based Niching

  • Mike Preuss
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

Here we employ the nearest-better clustering basin identification method derived in a previous chapter for setting up two niching evolutionary algorithms. After doing parameter testing, we investigate how these algorithms perform in comparison to other recent methods for the all-global and one-global use cases by means of available benchmark suites.

Keywords

Local Search Differential Evolution Search Point Covariance Matrix Adaptation Evolution Strategy Initial Sample Size 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Mike Preuss
    • 1
  1. 1.Lehrstuhl für Wirtschaftsinformatik und StatistikWestfälische Wilhelms-Universität MünsterMünsterGermany

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