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- Benjamin Doerr

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
41
Citations
6,657
226
World Ranking
4312
National Ranking
198

Mathematics
D-index
41
Citations
5,900
214
World Ranking
952
National Ranking
57

- Algorithm
- Combinatorics
- Algebra

His scientific interests lie mostly in Evolutionary algorithm, Algorithm, Combinatorics, Mathematical optimization and Function. The concepts of his Evolutionary algorithm study are interwoven with issues in Discrete mathematics, Heuristics, Mutation rate, Evolutionary computation and Upper and lower bounds. His study in the field of Time complexity also crosses realms of Test functions for optimization.

His Binary logarithm study in the realm of Combinatorics interacts with subjects such as Value. His study focuses on the intersection of Mathematical optimization and fields such as Shortest path problem with connections in the field of Human-based evolutionary computation and Interactive evolutionary computation. His Function research includes themes of Linear function, Spanning tree and Constant.

- Theory of Randomized Search Heuristics: Foundations and Recent Developments (256 citations)
- Why rumors spread so quickly in social networks (248 citations)
- Multiplicative Drift Analysis (194 citations)

The scientist’s investigation covers issues in Combinatorics, Evolutionary algorithm, Discrete mathematics, Algorithm and Upper and lower bounds. He interconnects Rounding and Constant in the investigation of issues within Combinatorics. His Evolutionary algorithm research incorporates themes from Function, Genetic algorithm, Evolutionary computation and Heuristics.

His Function research integrates issues from Black box, Simple, Mutation rate, Applied mathematics and Benchmark. His Discrete mathematics research is multidisciplinary, relying on both Linear programming, Expected value and Random walk. His work on Asymptotically optimal algorithm and Randomized algorithm as part of general Algorithm study is frequently linked to Test functions for optimization, therefore connecting diverse disciplines of science.

- Combinatorics (46.47%)
- Evolutionary algorithm (33.58%)
- Discrete mathematics (25.06%)

- Evolutionary algorithm (33.58%)
- Genetic algorithm (11.19%)
- Function (16.79%)

His primary areas of study are Evolutionary algorithm, Genetic algorithm, Function, Upper and lower bounds and Combinatorics. His Evolutionary algorithm study necessitates a more in-depth grasp of Mathematical optimization. His studies in Genetic algorithm integrate themes in fields like Algorithm, Distribution, Mutation and Exponential function.

His Function study incorporates themes from Evolutionary computation, Simple and Polynomial. Benjamin Doerr combines subjects such as Symmetry, Logarithm and Applied mathematics with his study of Upper and lower bounds. His study in the fields of Binary logarithm under the domain of Combinatorics overlaps with other disciplines such as Lambda.

- Analyzing randomized search heuristics via stochastic domination (46 citations)
- A tight runtime analysis for the cGA on jump functions: EDAs can cross fitness valleys at no extra cost (33 citations)
- Theory of evolutionary computation : recent developments in discrete optimization (27 citations)

- Algorithm
- Algebra
- Combinatorics

His primary areas of investigation include Evolutionary algorithm, Function, Genetic algorithm, Mathematical optimization and Benchmark. His work carried out in the field of Evolutionary algorithm brings together such families of science as Discrete mathematics, Multiplicative function, Probabilistic logic, Mutation rate and Constant. His Genetic algorithm research incorporates elements of EDAS, Estimation of distribution algorithm, Algorithm, Applied mathematics and Upper and lower bounds.

His work deals with themes such as Black box and Unary operation, which intersect with Algorithm. Benjamin Doerr conducts interdisciplinary study in the fields of Test functions for optimization and Combinatorics through his research. His studies deal with areas such as Matching and Order as well as Combinatorics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Why rumors spread so quickly in social networks

Benjamin Doerr;Mahmoud Fouz;Tobias Friedrich.

Communications of The ACM **(2012)**

389 Citations

Theory of Randomized Search Heuristics: Foundations and Recent Developments

Anne Auger;Benjamin Doerr.

**(2011)**

340 Citations

Multiplicative Drift Analysis

Benjamin Doerr;Daniel Johannsen;Carola Winzen.

Algorithmica **(2012)**

194 Citations

Social Networks Spread Rumors in Sublogarithmic Time

Benjamin Doerr;Mahmoud Fouz;Tobias Friedrich.

Electronic Notes in Discrete Mathematics **(2011)**

163 Citations

Crossover can provably be useful in evolutionary computation

Benjamin Doerr;Edda Happ;Christian Klein.

Theoretical Computer Science **(2012)**

134 Citations

From black-box complexity to designing new genetic algorithms

Benjamin Doerr;Carola Doerr;Franziska Ebel.

Theoretical Computer Science **(2015)**

129 Citations

Optimal fixed and adaptive mutation rates for the leadingones problem

Süntje Böttcher;Benjamin Doerr;Frank Neumann.

parallel problem solving from nature **(2010)**

127 Citations

Optimal Static and Self-Adjusting Parameter Choices for the $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm

Benjamin Doerr;Carola Doerr.

Algorithmica **(2018)**

116 Citations

Probabilistic Tools for the Analysis of Randomized Optimization Heuristics.

Benjamin Doerr.

arXiv: Data Structures and Algorithms **(2018)**

109 Citations

Stabilizing consensus with the power of two choices

Benjamin Doerr;Leslie Ann Goldberg;Lorenz Minder;Thomas Sauerwald.

acm symposium on parallel algorithms and architectures **(2011)**

99 Citations

Journal of Complexity

(Impact Factor: 1.333)

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