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Drift Analysis and Linear Functions Revisited

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44338

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44705

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45750

Winzen,  Carola
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Zitation

Doerr, B., Johannsen, D., & Winzen, C. (2010). Drift Analysis and Linear Functions Revisited. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010). - Pt. 3 (pp. 1967-1974). Piscataway, NJ: IEEE. doi:10.1109/CEC.2010.5586097.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1645-1
Zusammenfassung
We regard the classical problem how the (1+1)~Evolutionary Algorithm optimizes an arbitrary linear pseudo-Boolean function. We show that any such function is optimized in time ${(1+o(1)) 1.39 e n\ln (n)}$, where ${n}$ is the length of the bit string. We also prove a lower bound of ${(1-o(1))e n\ln(n)}$, which in fact holds for all functions with a unique global optimum. This shows that for linear functions, even though the optimization behavior might differ, the resulting runtimes are very similar. Our experimental results suggest that the true optimization times are even closer than what the theoretical guarantees promise.