What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Statistics
Dirk Sudholt focuses on Evolutionary algorithm, Mutation, Mathematical optimization, Function and Algorithm.
Dirk Sudholt combines subjects such as Evolutionary computation, Local search, Metaheuristic and Combinatorics with his study of Evolutionary algorithm.
His studies in Mutation integrate themes in fields like Theoretical computer science, Mutation rate and Crossover.
His Mutation rate research is multidisciplinary, relying on both Local optimum, Artificial intelligence and Premature convergence.
His work on Ant colony optimization algorithms is typically connected to Bounded function as part of general Mathematical optimization study, connecting several disciplines of science.
His research integrates issues of Contrast and Heuristics in his study of Algorithm.
His most cited work include:
- A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms (133 citations)
- Crossover is provably essential for the Ising model on trees (105 citations)
- Analysis of diversity-preserving mechanisms for global exploration* (80 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Evolutionary algorithm, Mathematical optimization, Algorithm, Function and Mutation.
His study in Evolutionary algorithm is interdisciplinary in nature, drawing from both Evolutionary computation, Genetic algorithm, Theoretical computer science and Heuristics.
His Mathematical optimization research includes themes of Time complexity and Artificial intelligence.
Dirk Sudholt interconnects Statistical hypothesis testing, Robustness and Benchmark in the investigation of issues within Algorithm.
His Mutation study combines topics in areas such as Variation, Selection, Mutation rate and Crossover.
His Ant colony optimization algorithms research is multidisciplinary, incorporating perspectives in Shortest path problem and Metaheuristic.
He most often published in these fields:
- Evolutionary algorithm (61.84%)
- Mathematical optimization (36.18%)
- Algorithm (36.18%)
What were the highlights of his more recent work (between 2017-2021)?
- Evolutionary algorithm (61.84%)
- Function (28.29%)
- Mathematical optimization (36.18%)
In recent papers he was focusing on the following fields of study:
Evolutionary algorithm, Function, Mathematical optimization, Local search and Heuristics are his primary areas of study.
His work carried out in the field of Evolutionary algorithm brings together such families of science as Local optimum, Crossover, Discrete mathematics and Mutation.
His Crossover study integrates concerns from other disciplines, such as Multi-objective optimization and Premature convergence.
His Tournament selection and Memetic algorithm study in the realm of Mathematical optimization connects with subjects such as Crowding.
His Local search study is focused on Algorithm in general.
His research on Heuristics also deals with topics like
- Parameter space, Mutation operator and Mutation rate most often made with reference to Benchmark,
- Graph most often made with reference to Graph coloring,
- Bipartite graph and related Hypercube.
Between 2017 and 2021, his most popular works were:
- Escaping Local Optima Using Crossover With Emergent Diversity (67 citations)
- Medium step sizes are harmful for the compact genetic algorithm (32 citations)
- The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses (21 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Statistics
Dirk Sudholt mostly deals with Evolutionary algorithm, Function, Time complexity, Mutation and Diversity.
His Evolutionary algorithm study deals with the bigger picture of Mathematical optimization.
His studies examine the connections between Time complexity and genetics, as well as such issues in Binary logarithm, with regards to Robustness and Hill climbing.
As part of the same scientific family, Dirk Sudholt usually focuses on Mutation, concentrating on Crossover and intersecting with Premature convergence and Mutation rate.
The various areas that he examines in his Combinatorics study include Probability distribution, Ant colony optimization algorithms, Global optimization and Metaheuristic.
His Algorithm research is multidisciplinary, incorporating elements of Expected value and Simple random sample.
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