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Many experimental results are reported on all types of Evolutionary Algorithms but only few results have been proved. A step towards a theory on Evolutionary Algorithms, in particular, the so-called (1 + 1) Evolutionary Algorithm, is performed. Linear functions are proved to be optimized in expected time O(n lnn) but only mutation rates of size Θ(1/n) can… (More)

- Ingo Wegener
- 1987

- Beate Bollig, Ingo Wegener
- IEEE Trans. Computers
- 1996

- Ingo Wegener
- 2000

- Stefan Droste, Thomas Jansen, Ingo Wegener
- Theory of Computing Systems
- 2003

Randomized search heuristics like local search, tabu 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… (More)

- Ingo Wegener
- 2000

Many experiments have shown that evolutionary algorithms are useful randomized search heuristics for optimization problems. In order to learn more about the reasons for their efficiency and in order to obtain proven results on evolutionary algorithms it is necessary to develop a theory of evolutionary algorithms. Such a theory is still in its infancy. A… (More)

- Thomas Jansen, Kenneth A. De Jong, Ingo Wegener
- Evolutionary Computation
- 2005

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified… (More)

- Jens Scharnow, Karsten Tinnefeld, Ingo Wegener
- J. Math. Model. Algorithms
- 2004

- Thomas Jansen, Ingo Wegener
- IEEE Trans. Evolutionary Computation
- 2001

The most simple evolutionary algorithm, the so-called (1+1)EA accepts a child if its fitness is at least as large (in the case of maximization) as the fitness of its parent. The variant (1 + 1)∗EA only accepts a child if its fitness is strictly larger than the fitness of its parent. Here two functions related to the class of long path functions are… (More)

- Oliver Giel, Ingo Wegener
- STACS
- 2003