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.
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.
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.
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)
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Anne Auger;Benjamin Doerr.
(2011)
Multiplicative Drift Analysis
Benjamin Doerr;Daniel Johannsen;Carola Winzen.
Algorithmica (2012)
Social Networks Spread Rumors in Sublogarithmic Time
Benjamin Doerr;Mahmoud Fouz;Tobias Friedrich.
Electronic Notes in Discrete Mathematics (2011)
Crossover can provably be useful in evolutionary computation
Benjamin Doerr;Edda Happ;Christian Klein.
Theoretical Computer Science (2012)
From black-box complexity to designing new genetic algorithms
Benjamin Doerr;Carola Doerr;Franziska Ebel.
Theoretical Computer Science (2015)
Optimal fixed and adaptive mutation rates for the leadingones problem
Süntje Böttcher;Benjamin Doerr;Frank Neumann.
parallel problem solving from nature (2010)
Optimal Static and Self-Adjusting Parameter Choices for the $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm
Benjamin Doerr;Carola Doerr.
Algorithmica (2018)
Probabilistic Tools for the Analysis of Randomized Optimization Heuristics.
Benjamin Doerr.
arXiv: Data Structures and Algorithms (2018)
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)
Journal of Complexity
(Impact Factor: 1.333)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Hasso Plattner Institute
University of Adelaide
Technical University of Denmark
New York University
University of Sheffield
Aberystwyth University
University of Oxford
Japan Advanced Institute of Science and Technology
Max Planck Institute for Informatics
Alfréd Rényi Institute of Mathematics
Brunel University London
Colorado State University
Carnegie Mellon University
University of Tokyo
Nanjing University
North Carolina State University
Cardiff University
King's College London
University of California, Berkeley
Cornell University
University of Iceland
Institut Pasteur
Indiana University
Ruhr University Bochum
University of Alabama at Birmingham
University of Massachusetts Amherst