• Publications
  • Influence
On the analysis of the (1+1) evolutionary algorithm
TLDR
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
TLDR
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
New Results on Visual Cryptography
TLDR
A new principle of construction for k out of n secret sharing schemes is presented which is easy to apply and in most cases gives much better results than the former principlcs. Expand
A rigorous analysis of the compact genetic algorithm for linear functions
  • Stefan Droste
  • Mathematics, Computer Science
  • Natural Computing
  • 1 September 2006
TLDR
First rigorous runtime analyses of a simple EDA, the compact genetic algorithm (cGA), for linear pseudo-Boolean functions on n variables are presented, proving a general lower bound for all functions and a general upper bound forall linear functions. Expand
Analysis of the (1+1) EA for a Noisy OneMax
A combination tow and pressure relief valve for use in a hydraulic fluid circuit used in a hydraulically driven wide area lawn mower. The valve includes a cylindrical valve body having a hexagonalExpand
Analysis of the (1+1) EA for a dynamically changing ONEMAX-variant
  • Stefan Droste
  • Computer Science
  • Proceedings of the Congress on Evolutionary…
  • 12 May 2002
TLDR
The main focus lies on determining the degree of change of the fitness function, where the expected runtime of the (1+1) EA rises from polynomial to super-polynomial. Expand
Optimization with randomized search heuristics - the (A)NFL theorem, realistic scenarios, and difficult functions
TLDR
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
TLDR
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
A Rigorous Complexity Analysis of the (1 + 1) Evolutionary Algorithm for Separable Functions with Boolean Inputs
TLDR
The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is (n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously. Expand
Analysis of the (1+1) EA for a Dynamically Bitwise Changing OneMax
TLDR
The movement rate of the target bit string is computed resulting in a polynomial expected first hitting time of the (1+1) EA asymptotically exactly, which strengthens a previous result, where the dynamically changing OneMax changed only at most one bit at a time. Expand
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