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Conference Paper

How Single Ant ACO Systems Optimize Pseudo-Boolean Functions

MPS-Authors
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Johannsen,  Daniel
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Doerr,  Benjamin
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

/persons/resource/persons45591

Tang,  Ching Hoo
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Citation

Johannsen, D., Doerr, B., & Tang, C. H. (2008). How Single Ant ACO Systems Optimize Pseudo-Boolean Functions. In G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, & N. Beume (Eds.), Parallel Problem Solving from Nature – PPSN X (pp. 378-388). Berlin: Springer. doi:10.1007/978-3-540-87700-4_38.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1BE1-A
Abstract
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.