A step towards a theory on Evolutionary Algorithms, in particular, the so-called (1+1) evolutionary Algorithm, is performed and linear functions are proved to be optimized in expected time O(nlnn) but only mutation rates of size (1/n) can ensure this behavior.Expand

Lower bounds on the black-box complexity of problems are derived without complexity theoretical assumptions and are compared with upper bounds in this scenario.Expand

U-relations is introduced, a succinct and purely relational representation system for uncertain databases that support attribute-level uncertainty using vertical partitioning and shows that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.Expand

A pair of skis are provided on their upper surfaces with respective mounting plates each carrying a treadle depressible by the boot of the user, the treadle overlying the bight of a yoke biased into… Expand

It is proved that an evolutionary algorithm can produce enough diversity such that the use of crossover can speedup the expected optimization time from superpolynomial to a polynomial of small degree.Expand

Two different types of classifications of fitness functions are distinguished, descriptive and analytical ones, and three widely known approaches are discussed, namely the NK-model, epistasis variance, and fitness distance correlation.Expand

The author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics with a complexity-theoretical perspective, derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms.Expand

Two simple randomised search heuristics, random local search and the (1+1) evolutionary algorithm, are analysed on some well-known example problems and upper and lower bounds on the expected quality of a solution for a fixed budget of function evaluations are proven.Expand

An Almost No Free Lunch (ANFL) theorem shows that for each function which can be optimized efficiently by a search heuristic there can be constructed many related functions where the same heuristic is bad.Expand

It can be concluded that randomized search heuristics whose (worst-case) expected optimization time for some problem is close to the black-box complexity of the problem are provably efficient (in theblack-box scenario).Expand