Un I Vers I Ty Of Dortmund (2007)
Re He Computat, Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, ...
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...
The Analysis of a Recombinative Hill-Climber on (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Clarissa Van Hoyweghen, Ingo Wegener, ...
Abstract--- Many experiments have proved that crossover is an essential search operator in evolutionary algorithms, at least for certain functions. However, the rigorous analysis of such algorithms...
Evolutionary Algorithms and the Maximum Matching Problem (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Oliver Giel, ...
Abstract. Randomized search heuristics like evolutionary algorithms are mostly applied to problems whose structure is not completely known but also to combinatorial optimization problems....
Analysis of a Simple Evolutionary Algorithm for the Minimization in Euclidian Spaces (2007)
Re I He, Computat I Onal, I Ntell, I Gence
Abstract. Although evolutionary algorithms (EAs) are widely used in practical optimization, their theoretical analysis is still in its infancy. Up to now results on expected runtimes and success...
How to analyse evolutionary algorithms (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Hans-georg Beyer, Hans-paul Schwefel, ...
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by Simple Randomized Search Heuristics (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener, ...
This work is a product of the Collaborative Research Center 531, "Computational
A Hybrid Approach to Feature Selection and Generation Using an Evolutionary Algorithm (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Oliver Ritthoff, Oliver Ritthoff, ...
Abstract. Genetic algorithms proved to work well on feature selection problems where the search space produced by the initial feature set already contains the hypothesis to be learned. In cases where...
A New Framework for the Valuation of Algorithms for Black-Box Optimization (2007)
Re I He, Computat I Onal, I Ntell, I Gence, For Black-box-optimization, Thomas Jansen, ...
Black-box optimization algorithms cannot use the specific parameters of the problem instance, i.e., of the fitness function f. Their run time is measured as the number of f-evaluations. This implies...
A Hybrid Approach to Feature Selection and Generation Using an Evolutionary Algorithm (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Oliver Ritthoff, Oliver Ritthoff, ...
Abstract. Genetic algorithms proved to work well on feature selection problems where the search space produced by the initial feature set already contains the hypothesis to be learned. In cases where...
THEORETICAL ASPECTS OF EVOLUTIONARY ALGORITHMS (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener
Abstract. Randomized search heuristics like simulated annealing and evolutionary algorithms are applied successfully in many di#erent situations. However, the theory on these algorithms is still in...
Analysis of the (1+1) EA for a Dynamically Changing Objective Function (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Stefan Droste
This work is a product of the Collaborative Research Center 531, "Computational
Secretary of the SFB 531 (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Thomas Jansen, Thomas Jansen, ...
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...
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Thomas Jansen, ...
Evolutionary algorithms (EAs) are randomized search strategies which have turned out to be e#cient for optimization problems of quite di#erent kind. In order to understand the behavior of EAs, one...
Secretary of the SFB 531 (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Thomas Jansen, Thomas Jansen
This work is a product of the Collaborative Research Center 531, "Computational
On the design and analysis of evolutionary algorithms # (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener
Abstract: Evolutionary algorithms are problem-independent randomized search heuristics. It is discussed when it is useful to work with such algorithms and it is argued why these search heuristics...
Dynamic Parameter Control in Simple Evolutionary Algorithms (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Thomas Jansen, ...
Evolutionary algorithms are general, randomized search heuristics that are influenced by many parameters. Though evolutionary algorithms are assumed to be robust, it is well-known that choosing the...
Real Royal Road Functions--- Where Crossover Provably is Essential # (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener
Mutation and crossover are the main search operators of di#erent variants of evolutionary algorithms. Despite the many discussions on the importance of crossover nobody has proved rigorously for some...
Algorithm on Quadratic Pseudo-Boolean Functions # (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener, ...
This work is a product of the Collaborative Research Center 531, "Computational
Distributed Hybrid Genetic Programming for Learning Boolean Functions (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Dominic Heutelbeck, Ingo Wegener, ...
This work is a product of the Collaborative Research Center 531, "Computational
PSEUDO-BOOLEAN FUNCTIONS (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener
This work is a product of the Collaborative Research Center 531, "Computational
Secretary of the SFB 531 (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Thomas Jansen, Thomas Jansen, ...
This work is a product of the Collaborative Research Center 531, "Computational
ON THE EXPECTED RUNTIME AND THE SUCCESS PROBABILITY OF EVOLUTIONARY ALGORITHMS (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Ingo Wegener, Ingo Wegener
This work is a product of the Collaborative Research Center 531, "Computational
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Thomas Jansen, ...
This work is a product of the Collaborative Research Center 531, "Computational
PERHAPS NOT A FREE LUNCH BUT AT LEAST (2007)
Re I He, Computat I Onal, I Ntell, I Gence, A Free Appetizer, Stefan Droste, ...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in particular, in situations where not much is known about the objective function to be optimized. In...
Un I Vers I Ty Of Dortmund (2007)
Re He Computat, Re I He, Computat I Onal, I Ntell, I Gence, Thomas Jansen, ...
Coevolutionary algorithms are a variant of evolutionary algorithms which are aimed for the solution of more complex tasks than traditional evolutionary algorithms. One example is a general...
Analysis of the (1+1) EA for a Dynamically Bitwise Changing OneMax (2007)
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Stefan Droste
This work is a product of the Collaborative Research Center 531, "Computational
On the Classification of Fitness Functions (1999)
Re I He, Computat I Onal, I Ntell, I Gence, Thomas Jansen, Thomas Jansen
It is well-known that evolutionary algorithms succeed to optimize some functions efficiently and fail for others. Therefore, one would like to classify fitness functions as more or less hard to...
Approximations by OBDDs and the Variable Ordering Problem (1999)
Re I He, Petr Savicky, Computat I Onal, I Ntell, I Gence, Matthias Krause, ...
Ordered binary decision diagrams (OBDDs) and their variants are motivated by the need to represent Boolean functions in applications. Research concerning these applications leads also to problems and...
Ease - Evolutionary Algorithms Scripting Environment (1999)
Re I He, Computat I Onal, I Ntell, I Gence, Joachim Sprave
this document is familiar with the basics of the Tcl scripting language. Furthermore, basic knowledge of the C programming language is required to implement user-defined fitness functions. General...
A Unified Model of Non-Panmictic Population Structures in Evolutionary Algorithms (1999)
Re I He, Computat I Onal, I Ntell, I Gence, Joachim Sprave
This paper starts with a brief overview of other approaches to model, classify, or analyze population structures. Then, a general framework for the formal description of population structures is...
On the Analysis of a Simple Evolutionary Algorithm With Dynamic Parameter Control (1999)
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Stefan Droste, ...
Evolutionary algorithms usually are controlled by various parameters and it is well known that an appropriate choice of these control parameters is crucial for the eciency of the algorithms. In many...
On the Analysis of the (1+1) Evolutionary Algorithm (1998)
Re I He, Computat I Onal, I Ntell, I Gence, Stefan Droste, Stefan Droste, ...
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...
On the analysis of evolutionary algorithms - A proof that crossover really can help (1998)
Re I He, Computat I Onal, I Ntell, I Gence, Thomas Jansen, Thomas Jansen, ...
There is a lot of experimental evidence that crossover is, for some functions, an essential operator of evolutionary algorithms. Nevertheless, it was an open problem to prove for some function that...