Learn More
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 ln n) but only mutation rates of size Θ(1/n) can(More)
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)
Randomized search heuristics, among them randomized local search and evolutionary algorithms, are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. The analysis of these randomized search heuristics has been started for some well-known problems, and this approach is followed here for the(More)
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)