How Single Ant ACO Systems Optimize Pseudo-Boolean Functions

  • Benjamin Doerr
  • Daniel Johannsen
  • Ching Hoo Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


We undertake a rigorous experimental analysis of the optimization behavior of the two most studied single ant ACO systems on several pseudo-boolean functions. By tracking the behavior of the underlying random processes rather than just regarding the resulting optimization time, we gain additional insight into these systems. A main finding is that in those cases where the single ant ACO system performs well, it basically simulates the much simpler (1+1) evolutionary algorithm.


Optimization Behavior Runtime Analysis Evaporation Factor Randomize Search Heuristic Expect Optimization Time 
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 2008

Authors and Affiliations

  • Benjamin Doerr
    • 1
  • Daniel Johannsen
    • 1
  • Ching Hoo Tang
    • 1
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany

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