Stefan Droste, Thomas Jansen, Ingo Wegener

Randomized search heuristics like local search, simulated annealing or all kinds of evolutionary algorithms have many applications. However, for most problems the best worst-case expected run times are achieved by more problem-specific algorithms. This raises the question about the limits of general randomized search heuristics.

Here a framework called black-box ... more >>>

Oliver Giel, Ingo Wegener

Many real-world optimization problems in, e.g., engineering

or biology have the property that not much is known about

the function to be optimized. This excludes the application

of problem-specific algorithms. Simple randomized search

heuristics are then used with surprisingly good results. In

order to understand the working principles behind such

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Nils Hebbinghaus, Benjamin Doerr, Frank Neumann

We investigate the effect of restricting the mutation operator in

evolutionary algorithms with respect to the runtime behavior.

Considering the Eulerian cycle problem we present runtime bounds on

evolutionary algorithms with a restricted operator that are much

smaller than the best upper bounds for the ...
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