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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. Expand
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The complexity of Boolean functions
Introduction to the Theory of Boolean Functions and Circuits. The Minimimization of Boolean Functions. The Design of Efficient Circuits for Some Fundamental Functions. Asymptotic Results andExpand
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Improving the Variable Ordering of OBDDs Is NP-Complete
Ordered binary decision diagrams are a useful representation of Boolean functions, if a good variable ordering is known. Variable orderings are computed by heuristic algorithms and then improved withExpand
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Branching Programs and Binary Decision Diagrams
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Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
TLDR
This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center “Computational Intelligence” (SFB 531). Expand
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On the Choice of the Offspring Population Size in Evolutionary Algorithms
TLDR
Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. Expand
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The analysis of evolutionary algorithms on sorting and shortest paths problems
TLDR
The analysis of evolutionary algorithms is up to now limited to special classes of functions and fitness landscapes. Expand
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Branching Programs and Binary Deci-sion Diagrams-Theory and Applications
Preface Introduction 1. Introduction 2. BPs and Decision Trees (DTs) 3. Ordered Binary Decision Diagrams (OBDDs) 4. The OBDD Size of Selected Functions 5. The Variable-Ordering Problem 6. Free BDDsExpand
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Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions
TLDR
This paper focusses on expected run times and the success probability within reasonable time bounds of evolutionary algorithms. Expand
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The Analysis of Evolutionary Algorithms—A Proof That Crossover Really Can Help
TLDR
We prove 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
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