Effects of Scale-Free and Small-World Topologies on Binary Coded Self-adaptive CEA

  • Mario Giacobini
  • Mike Preuss
  • Marco Tomassini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)

Abstract

In this paper we investigate the properties of CEAs with populations structured as Watts–Strogatz small-world graphs and Albert–Barabási scale-free graphs as problem solvers, using several standard discrete optimization problems as a benchmark. The EA variants employed include self-adaptation of mutation rates. Results are compared with the corresponding classical panmictic EA showing that topology together with self-adaptation drastically influences the search.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mario Giacobini
    • 1
  • Mike Preuss
    • 2
  • Marco Tomassini
    • 1
  1. 1.Information Systems DepartmentUniversity of LausanneSwitzerland
  2. 2.Systems Analysis Group, Computer Science DepartmentUniversity of DortmundGermany

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