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- Carsten Witt

Discipline name
D-index
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Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
37
Citations
5,074
104
World Ranking
5255
National Ranking
18

- Algorithm
- Mathematical analysis
- Artificial intelligence

His primary areas of investigation include Evolutionary algorithm, Mathematical optimization, Algorithm, Heuristics and Crossover. The study incorporates disciplines such as Combinatorics, Mutation, Function, Upper and lower bounds and Polynomial in addition to Evolutionary algorithm. His study ties his expertise on Time complexity together with the subject of Mathematical optimization.

His study in Algorithm focuses on Theory of computation and Ant colony optimization algorithms. His research investigates the connection between Heuristics and topics such as Simulated annealing that intersect with problems in Unimodality, Local search and Black box. His Crossover research integrates issues from Genetic algorithm, Simulation, Population size and Selection.

- Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity (251 citations)
- Runtime Analysis of the ( μ +1) EA on Simple Pseudo-Boolean Functions (188 citations)
- Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions (163 citations)

His main research concerns Evolutionary algorithm, Mathematical optimization, Algorithm, Heuristics and Function. Carsten Witt combines subjects such as Time complexity, Local search, Mutation, Benchmark and Evolutionary computation with his study of Evolutionary algorithm. His study in the field of Ant colony optimization algorithms, Optimization problem, Combinatorial optimization and Search algorithm also crosses realms of Simple.

Population size is closely connected to Crossover in his research, which is encompassed under the umbrella topic of Algorithm. His work on Randomized search as part of general Heuristics study is frequently connected to Variable, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. He has researched Function in several fields, including Fitness function, Combinatorics, Estimation of distribution algorithm, Exponential function and Upper and lower bounds.

- Evolutionary algorithm (55.33%)
- Mathematical optimization (39.33%)
- Algorithm (33.33%)

- Evolutionary algorithm (55.33%)
- Function (26.67%)
- Algorithm (33.33%)

His scientific interests lie mostly in Evolutionary algorithm, Function, Algorithm, Heuristics and Binary logarithm. His studies in Evolutionary algorithm integrate themes in fields like Local search and Mutation. His research in Function intersects with topics in Tournament selection, Selection, State, Benchmark and Upper and lower bounds.

As part of his studies on Algorithm, Carsten Witt often connects relevant areas like Crossover. His research on Heuristics concerns the broader Mathematical optimization. His Binary logarithm research includes themes of Asymptotic expansion and Theory of computation.

- Upper Bounds on the Running Time of the Univariate Marginal Distribution Algorithm on OneMax (22 citations)
- The ( $$1+\lambda $$ 1 + λ ) Evolutionary Algorithm with Self-Adjusting Mutation Rate (16 citations)
- On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization (16 citations)

- Algorithm
- Artificial intelligence
- Mathematical analysis

Carsten Witt mostly deals with Binary logarithm, Function, Evolutionary algorithm, Combinatorics and Benchmark. His Binary logarithm research is multidisciplinary, relying on both Mathematical optimization and Heuristics. He has included themes like Univariate marginal distribution algorithm, Estimation of distribution algorithm, Upper and lower bounds and Ant colony optimization algorithms in his Function study.

Carsten Witt applies his multidisciplinary studies on Evolutionary algorithm and Stochastic process in his research. The various areas that Carsten Witt examines in his Benchmark study include Discrete mathematics, Asymptotic expansion, State, Logarithm and Evolutionary computation. His biological study spans a wide range of topics, including Asymptotically optimal algorithm, Algorithm, Local optimum and Binary search algorithm.

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.

Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity

Frank Neumann;Carsten Witt.

**(2010)**

389 Citations

Runtime Analysis of the ( μ +1) EA on Simple Pseudo-Boolean Functions

Carsten Witt.

Evolutionary Computation **(2006)**

280 Citations

Bioinspired Computation in Combinatorial Optimization

Frank Neumann;Carsten Witt.

Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity **(2010)**

187 Citations

Black-box search by unbiased variation

Per Kristian Lehre;Carsten Witt.

genetic and evolutionary computation conference **(2010)**

177 Citations

Worst-case and average-case approximations by simple randomized search heuristics

Carsten Witt.

symposium on theoretical aspects of computer science **(2005)**

174 Citations

Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions

Carsten Witt.

Combinatorics, Probability & Computing **(2013)**

163 Citations

Runtime Analysis of a Simple Ant Colony Optimization Algorithm

Frank Neumann;Carsten Witt.

Algorithmica **(2009)**

159 Citations

Approximating covering problems by randomized search heuristics using multi-objective models*

Tobias Friedrich;Jun He;Nils Hebbinghaus;Frank Neumann.

Evolutionary Computation **(2010)**

144 Citations

Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

Pietro S. Oliveto;Carsten Witt.

Algorithmica **(2011)**

131 Citations

Combinatorial Optimization and Computational Complexity

Frank Neumann;Carsten Witt.

**(2010)**

120 Citations

University of Adelaide

University of Sheffield

Max Planck Institute for Informatics

Hasso Plattner Institute

Aberystwyth University

University of Copenhagen

CINVESTAV

Colorado State University

TU Wien

Technical University of Munich

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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