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On the analysis of the (1+1) evolutionary algorithm
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
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
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
Fast and Simple Relational Processing of Uncertain Data
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
Evolutionary algorithms - how to cope with plateaus of constant fitness and when to reject strings of the same fitness
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 intoExpand
The Analysis of Evolutionary Algorithms—A Proof That Crossover Really Can Help
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
On classifications of fitness functions
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
Analyzing Evolutionary Algorithms: The Computer Science Perspective
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
Performance analysis of randomised search heuristics operating with a fixed budget
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
Optimization with randomized search heuristics - the (A)NFL theorem, realistic scenarios, and difficult functions
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
A New Framework for the Valuation of Algorithms for Black-Box Optimization
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