EA-Powered Basin Number Estimation by Means of Preservation and Exploration

  • Catalin Stoean
  • Mike Preuss
  • Ruxandra Stoean
  • Dumitru Dumitrescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


When using an evolutionary algorithm on an unknown problem, properties like the number of global/local optima must be guessed for properly picking an algorithm and its parameters. It is the aim of current paper to put forward an EA-based method for real-valued optimization to provide an estimate on the number of optima a function exhibits, or at least of the ones that are in reach for a certain algorithm configuration, at low cost. We compare against direct clustering methods applied to different stages of evolved populations; interestingly, there is a turning point (in evaluations) after which our method is clearly better, although for very low budgets, the clustering methods have advantages. Consequently, it is argued in favor of further hybridizations.


Multimodal optimization basins of attraction function optimization detect-multimodal mechanism 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Catalin Stoean
    • 1
  • Mike Preuss
    • 2
  • Ruxandra Stoean
    • 1
  • Dumitru Dumitrescu
    • 3
  1. 1.Department of Computer Science, Faculty of Mathematics and Computer ScienceUniversity of CraiovaRomania
  2. 2.Chair of Algorithm Engineering, Department of Computer ScienceDortmund University of TechnologyGermany
  3. 3.Department of Computer Science, Faculty of Mathematics and Computer ScienceBabes-Bolyai University of Cluj-NapocaRomania

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