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)


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.


Mutation Rate Random Graph Average Path Length Kernel Size Regular Lattice 
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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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