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Optimizing Monotone Functions Can Be Difficult

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/persons45750

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

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Zitation

Doerr, B., Jansen, T., Sudholt, D., Winzen, C., & Zarges, C. (2010). Optimizing Monotone Functions Can Be Difficult. In R. Schaefer, C. Cotta, J. Kolodziej, & G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI (pp. 42-51). Berlin: Springer. doi:10.1007/978-3-642-15844-5_5.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-16AC-C
Zusammenfassung
Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotone. Contrary to what one would expect, not all of these functions are easy to optimize. The choice of the constant $c$ in the mutation probability $p(n) = c/n$ can make a decisive difference. We show that if $c < 1$, then the \EA finds the optimum of every such function in $\Theta(n \log n)$ iterations. For $c=1$, we can still prove an upper bound of $O(n^{3/2})$. However, for $c > 33$, we present a strictly monotone function such that the \EA with overwhelming probability does not find the optimum within $2^{\Omega(n)}$ iterations. This is the first time that we observe that a constant factor change of the mutation probability changes the run-time by more than constant factors.