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 >>>

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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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 ... more >>>