What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Algebra
His main research concerns Mathematical optimization, Holocene, Evolutionary algorithm, Climate change and Palynology.
In his study, Point and Combinatorics is strongly linked to Algorithm, which falls under the umbrella field of Mathematical optimization.
His study focuses on the intersection of Holocene and fields such as Shore with connections in the field of Transect.
Frank Neumann has included themes like Function, Evolutionary computation, Time complexity and Heuristic in his Evolutionary algorithm study.
His Climate change study combines topics in areas such as Climatology, Quaternary and Fire regime.
The concepts of his Palynology study are interwoven with issues in Mediterranean climate, Sea surface temperature and Vegetation.
His most cited work include:
- Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics (358 citations)
- Predictability of biomass burning in response to climate changes (291 citations)
- Predictability of biomass burning in response to climate changes (291 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Mathematical optimization, Evolutionary algorithm, Evolutionary computation, Optimization problem and Multi-objective optimization.
His Point research extends to Mathematical optimization, which is thematically connected.
His Evolutionary algorithm research is multidisciplinary, incorporating elements of Algorithm, Theoretical computer science, Heuristics and Vertex cover.
The study incorporates disciplines such as Time complexity and Search algorithm in addition to Heuristics.
To a larger extent, Frank Neumann studies Artificial intelligence with the aim of understanding Evolutionary computation.
His Combinatorial optimization research incorporates themes from Minimum spanning tree and Spanning tree.
He most often published in these fields:
- Mathematical optimization (44.59%)
- Evolutionary algorithm (41.61%)
- Evolutionary computation (15.29%)
What were the highlights of his more recent work (between 2018-2021)?
- Evolutionary algorithm (41.61%)
- Mathematical optimization (44.59%)
- Evolutionary computation (15.29%)
In recent papers he was focusing on the following fields of study:
Frank Neumann mainly investigates Evolutionary algorithm, Mathematical optimization, Evolutionary computation, Constraint and Knapsack problem.
His Evolutionary algorithm study combines topics from a wide range of disciplines, such as Theoretical computer science, Local search, Travelling salesman problem, Heuristics and Graph.
Many of his studies on Mathematical optimization involve topics that are commonly interrelated, such as Key.
His studies in Evolutionary computation integrate themes in fields like Discrete optimization and Spanning tree.
His Constraint research integrates issues from Function, Upper and lower bounds, Chernoff bound and Theory of computation.
The various areas that he examines in his Knapsack problem study include Range, Structure, Crossover and Multi-objective optimization.
Between 2018 and 2021, his most popular works were:
- Automated Algorithm Selection: Survey and Perspectives (101 citations)
- Theory of evolutionary computation : recent developments in discrete optimization (27 citations)
- Robust Fitting in Computer Vision: Easy or Hard? (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical optimization
The scientist’s investigation covers issues in Evolutionary algorithm, Mathematical optimization, Constraint, Knapsack problem and Evolutionary computation.
His Evolutionary algorithm research incorporates elements of Dynamic problem, Travelling salesman problem, Theoretical computer science and Heuristics.
Frank Neumann is studying Optimization problem, which is a component of Mathematical optimization.
His Optimization problem study integrates concerns from other disciplines, such as Point, Theory of computation and Ant colony optimization algorithms.
His Constraint study incorporates themes from Function, Submodular set function, Chernoff bound and Greedy algorithm.
His Knapsack problem research focuses on Range and how it connects with Polynomial-time approximation scheme, Traveling purchaser problem, Stochastic optimization and Dynamic programming.
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